This paper introduces EasyText, a fully operational NLG system. This application processes numerical data (in tables) in order to generate specific analytical commentaries of these tables. We start by describing the context of this particular NLG application (communicative goal, user profiles, etc.). We then shortly present the theoretical background which underlies EasyText, before describing its implementation, realization and evaluation. 

Frédéric Meunier

Laurence Danlos

Vanessa Combet

International audienceThis paper introduces EasyText, a fully operational NLG System. This application processes numerical data (in tables) in order to generate specific analytical comments of these tables. We start by describing the context of this particular NLG application (communicative goal, user profiles, etc.). We then shortly present the theoretical background which underlies EasyText, before describing its implementation and realization

Danlos, Laurence

Meunier, Frédéric

Combet, Vanessa

Hal-Diderot

EasyText: an Operational NLG SystemLaurence Danlos, Fre´de´ric Meunier, Vanessa CombetTo cite this version:Laurence Danlos, Fre´de´ric Meunier, Vanessa Combet. EasyText: an Operational NLG System.ENLG 2011 - 13th European Workshop on Natural Language Generation, Sep 2011, Nancy,France. 2011. <inria-00614760>HAL Id: inria-00614760https://hal.inria.fr/inria-00614760Submitted on 16 Aug 2011HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestine´e au de´poˆt et a` la diffusion de documentsscientifiques de niveau recherche, publie´s ou non,e´manant des e´tablissements d’enseignement et derecherche franc¸ais ou e´trangers, des laboratoirespublics ou prive´s.EasyText : an Operational NLG SystemLaurence DanlosUniv Paris Diderot,Sorbonne Paris Cite´,ALPAGE, UMR-I 001 INRIADanlos@linguist.jussieu.frFre´de´ric MeunierWatchSystem AssistanceMeunier@watchsystance.comVanessa CombetWatchSystem AssistanceCombet@watchsystance.comAbstractThis paper introduces EasyText, a fully oper-ational NLG System. This application pro-cesses numerical data (in tables) in order togenerate specific analytical comments of thesetables. We start by describing the context ofthis particular NLG application (communica-tive goal, user profiles, etc.). We then shortlypresent the theoretical background which un-derlies EasyText, before describing its imple-mentation and realization.1 IntroductionEasyText is a NLG system which is operationalat Kantar Media, a French subsidiary company ofTNS-Sofres1. The company compiles numericaldata for its customers on their advertising invest-ments and sends to each customer seven tables ev-ery month, see Figure 1 for an example of a table.Before the existence of EasyText, these tables werepresented with a general commentary written by amedia analyst. Kantar decided to accompany thesetables with specific charts and comments in orderto make their reading easier. They survey 600 seg-ments and there are 7 tables per segment: Manu-ally writing these analytical comments was incon-ceivable. The idea of having an automatic systemproducing them naturally arose, but Kantar encoun-tered major difficulties with the text generation task.Therefore, they subcontracted this project to Watch1Kantar Media is one of the leaders in advertising expen-diture monitoring, exploring all existing media (radio, internet,mobile telephony, etc.).System Assistance. Figure 1 shows an example ofan analytical comment generated by EasyText.Section 2 describes the architecture of EasyText.Section 3 presents its implementation: EasyText isan instantiation for Kantar Media’s needs of a ready-to-use NLG framework, TextElaborator. Section 4gives some details on the realization and evaluationof EasyText.2 Architecture of EasyTextEasyText follows a standard architecture as de-scribed in (Reiter and Dale, 2000). It includes adocument planner for the content determination anddocument structuring tasks, and a tactical compo-nent.The content determination task for a given ta-ble amounts to detecting the relevant cells of the ta-ble. This task was guided by business rules indicatedby Kantar Media’s analysts. These rules were hard-coded, i.e. without any reasoning module.The content of a cell is transformed into the con-ceptual representation of an eventuality whose pred-icate is given by the column heading. This predicatesubcategorizes two arguments, the first one corre-sponding to the line heading, the second one to thevalue of the cell. Therefore, the output of the contentdetermination module can be seen as a conjunctionof conceptual representations of eventualities.The document structuring task consists in in-troducing rhetorical relations between the semanticcontent of the highlighted cells. For instance, if twoopposite evolutions over a given period are observed(one decreasing, the other one increasing), the rela-tionContrast is introduced. On the contrary, wouldÉvolution des investissements par Secteur / VariétéInvestissements publicitaires plurimedia - Tri décroissant sur le cumul de l'année en cours - En k€Mai2008Mai2009Evol%Cumuljanvier à mai2008Cumuljanvier à mai2009Evol%ORGANISMES FINANCIERS 16 587 26 312 59 % 216 948 177 353 -18 %CREDIT PERSONNEL O.F 5 868 11 227 91 % 50 610 53 772 6 %~MULTIPROD.ORG.FINANCIERS 3 243 7 463 130 % 53 191 51 718 -3 %CREDIT RENOUVELABLE O.F 3 930 1 994 -49 % 60 094 34 987 -42 %INTERNET TELEMATIQUE 583 2 648 4 687 77 % 16 460 27 613 68 %RACHAT DE CREDITS O.F 777 732 -6 % 15 817 5 637 -64 %CREDIT AUTO MOTO O.F 79 110 39 % 5 638 993 -82 %CREDIT TRAVAUX O.F 86  535 797 49 %PARRAINAGE MECENAT O.F  80 0 -100 %Dans  votre  univers,  les  investissements  marquent  une  très  forte  progression  (+59%)  dans  le  secteur  ORGANISMES FINANCIERS en mai 2008 par rapport à mai 2007. Toutefois, pour le cumul à date de l'étude, ils connaissent une baisse de 18%.Dans ce secteur, les investissements ont doublé (+130%) pour la variété MULTIPROD.ORG.FINANCIERS en mai 2008 par  rapport à mai 2007. Par ailleurs, les investissements pour la variété CREDIT PERSONNEL O.F marquent une progression de  6%  pour  le  cumul  à  date  étudié.  Au  contraire,  pour  la  variété  MULTIPROD.ORG.FINANCIERS,  ils  voient  leur  volume diminuer (-3%) sur la même période.(Within your business area, ad spending ramps up (+59%) for sector ORGANISMES FINANCIERS in May 2008 compared with  May  2007.  However,  year  to  date,  it  falls  18%.  Within  this  sector,  ad  spending  doubles  (+130%)  for  segment  MULTI-PROD.ORG.FINANCIERS in May 2008 compared to May 2007. Furthermore, ad spending for segment CREDIT PERSONNEL  OF increases of 6% year to date. On the contrary, for segment MULTIPROD.ORG.FINANCIERS, it decreases (-3%) over the  same period.)Figure 1: Example of a table and its automatically generated commentthey have been going in the same direction, the re-lation Parallel would have been introduced, alongwith some hints to prepare an aggregation operationin the tactical component.The discourse theory on which the documentstructuring module relies is SDRT (Segmented Dis-course Representation Theory) (Asher, 1993; Asherand Lascarides, 2001), following (Danlos et al.,2001). The output of the document structuringcomponent is therefore consistent with a SDRS(SDRT structure), considered as a “conceptual” rep-resentation in which concepts (discourse relations,eventualities, entities) are embedded in a depen-dency structure (which is mathematically a DirectedAcyclic Graph).The tactical component (macro/micro-plannerand surface realizer) is based on G-TAG formal-ism (Danlos, 2001), the latter being itself foundedon lexicalized Tree Adjoining Grammars (TAG),(Joshi, 1985).2 G-TAG deals with the How to say it?2Since it was put forward by A. Joshi that TAG is an espe-cially well suited grammatical theory for text generation, adapt-ing TAG for generation has been widely explored, among manyothers, let us cite (Stone and Doran, 1997) and (Gardent andissue, understood as covering all (and only) linguis-tic decisions: segmentation of the text into sentencesand linear ordering of these sentences3, choice ofdiscourse connectives and other lexical items, syn-tactic constructions within sentences, aggregationoperations, referring expressions, semantic and syn-tactic parallelism, etc.The surface realizer is designed to use the syntac-tic and lexical information of a TAG grammar. ThisTAG grammar is extended to handle multi-sententialtexts and not only isolated sentences4. Therefore,the macro/mico planner is designed as a TAG exten-sion. More precisely, the architecture of G-TAG isoutlined in Figure 2:• The output of the macro/mico planner is a ”g-derivation tree”. In TAG, a derivation tree isKow, 2007). However, it is not in the scope of this paper tocompare G-TAG with these other approaches.3These tasks are not considered as part of the documentstructuring component. This is why the term macro/mico-planner is used in Figure 2.4The idea of extending TAG to handle multi-sentential textsis also used in text interpretation, e.g. D-LTAG (Webber, 2004)and D-STAG (Danlos, 2009).SDRSMacro/Micro Plannerg-derivation treeSurface Realizerg-derived treePost-processing moduleTextFigure 2: G-TAG tactical componentnot only seen as the history of the derivationbut also as a linguistic representation, close tosemantics, which can serve as a basis for adeeper semantic analysis (Kallmeyer, 2002). Ag-derivation tree in G-TAG is closer to seman-tics than a derivation tree in TAG: it is a se-mantic dependency tree annotated with syntac-tic information. Moreover, a g-derivation treerepresents a text while a derivation tree repre-sents a unique sentence.The macro/micro planner relies on lexicaldatabases associated with the various concepts(discourse relations, eventualities, entities) thatare relevant for the NLG application. A lexicaldatabase for a given element records the lex-emes lexicalizing it with their argument struc-ture, and the mappings between the conceptualand semantic arguments. With such a lexical-ized planner, the process for computing a g-derivation tree relies upon a single type of op-eration: lexicalization, i.e. choice of a lexemeand its syntactic realization to convey an in-stance of a concept. Since all the main deci-sions are made during this process, G-TAG canbe considered as a fully lexicalized formalismfor text generation.• Thanks to a TAG grammar (which specifiesthe mapping between the semantic and syntac-tic arguments), a g-derivation tree specifies aunique ”g-derived tree”, in the same way as aderivation tree specifies a unique derived tree.A g-derived tree is a syntactic tree annotatedwith morphological information.• From a g-derived tree, a post-processing mod-ule computes a text by performing morpholog-ical computations5 and formatting operations.Lexical databases for EasyText have been devel-oped by a linguist, V. Combet, who was workingin close collaboration with Kantar Media’s analysts.Particular attention was paid to linguistic variationin order to avoid producing tiresome texts for KantarMedia’s customers. This variation mainly concerns:• the lexical choices: the databases associatedto a given concept are as exhaustive as pos-sible. For example, the concept INCREASEwith a MAGNITUDE argument is lexical-ized either with the verb augmenter, doubleror tripler or with the light verb constructioneˆtre en hausse/augmentation or enregistrer unehausse/augmentation6. Moreover, a verb canbe modified with an adverb, e.g. faiblement,fortement, mode´remment for augmenter andpresque/pratiquement/plus que for doubler ortripler7, while the noun in a light verb con-struction can be modified with a preposed ad-jective, e.g. faible/forte, or a postposed one,e.g. mode´re´e8.• the order of the phrases: some phrases can ap-pear more or less freely in different places ina sentence. This is the case for duration ad-verbials such as pendant le mois de mai (dur-ing May) and also for different prepositionalphrases such as les investissements [pour lavarie´te´ X] augmentent [pour la varie´te´ X] (ad5Morphological operations include elisions (la augmenta-tion → l’augmentation) and contractions (de le mois → dumois).6In English, verbs increase, double or triple and light verbconstructions be on the increase or record an increase.7In English, adverbs slightly, seriously, moderately for in-crease and almost, nearly, more than for double or triple.8In English, an adjective is always preposed.spendings [for sector X] increase [for sectorX]).3 ImplementationA prototype of G-TAGwas first implemented in Ada(Meunier, 1997). G-TAG has been re-implementedas a ready-to-use framework, TextElaborator. Text-Elaborator is based on the Microsoft .Net frame-work. Particular attention was paid on functionaland business issues while taking advantage of .Netfor technical and non functional issues (persistence,reliability, scalability, etc.). We chose to rely onclassical design patterns9, which garantee an effort-less reusability of the different components.Our main implementation effort for TextElabora-tor was to build an IDE (Integrated Development En-vironment) incorporating tools which facilitate thelinguistic work, i.e. feeding, editing, debugging andtesting the various lexical databases — tasks which9DAO (Data Access Object, http://en.wikipedia.org/wiki/Data_access_object) and DTO (DataTransfer Object, http://en.wikipedia.org/wiki/Data_transfer_object) .are crucial in G-TAG not only in the developmentbut also the maintenance phases.A screenshot of this IDE is shown in Figure 3.The left column gives the domain ontology hier-archized as Abstract Objects (discourse relationsand eventualities) and Entities. When clicking ona concept of the domain ontology (e.g. Haussein Figure 3), the tab LexicalPredicates in-dicates the G-TAG lexical database associated withthis concept. When choosing an element of thisdatabase, the corresponding g-derivation tree is dis-played, along with some corollary information.EasyText is an instantiation of TextElaborator forKantar Media’s needs, constiting in an ontology andits corresponding lexical databases. TextElaboratoris written in C# language and is built and run uponthe Microsoft .Net framework. Thanks to .Net, itsintegration into Kantar Media’s information systemwas easy. Generating a comment as the one shownin Figure 1 requires an average of 400ms.Figure 3: Sceenshot of TextElaborator’s integrated development environment4 Realization and EvaluationThe development of EasyText took 7 mm (menmonth) altogether:• 1 mm was dedicated to the linguist’s training toTAG and G-TAG;• 1 mm was dedicated to interviews with Kan-tar’s media analysts;• 3 mm to design TextElaborator and its IDE;• 2 mm to fill the lexical databases.During these 7 months, we were never in con-tact with Kantar’s customers directly, but workedin closed interaction with the two departments in-volved in the project. On the one hand, we obvi-ously interacted with Kantar’s media analysts. Theyshared with us all their know-how on writing com-mentaries on Kantar’s tables, enabling us to cre-ate lexical databases corresponding to their editorialhabits.On the other hand, EasyText was developed inclose collaboration with Kantar’s Information sys-tem department, so as to meet their technical re-quirements: performance and compatibility with theexisting infrastructure.When we released the first version of EasyText,Kantar decided to send the automatically generatedcommentaries to a couple of customers, without say-ing anything about the way they have been written.These customers made some critics10 but gaveKantar Media the feedback that they were quitepleased with this offer. Therefore, Kantar Media de-cided in April 2010 to commercialize this new prod-uct and acknowledged that the commentaries wereautomatically generated. They keep on commercial-izing this service, which means that their clients aresatisfied.EasyText evaluation was made by Kantar’s me-dia analysts during several months. This evaluationwas qualitative and concerned the relevance of thecommentaries (the choice of the cells to comment)and their smoothness. We remind the reader (Sec-tion 1) that EasyText commentaries had never been10The main critic concerned the laying-out of these commen-taries.handwritten. Therefore, we cannot make any com-parison between the generated texts and handwrittenones. This situation seems to be quite usual, sinceNLG applications are likely to be commercializedwhen automatic writing doesn’t replace hand writ-ing11. Indeed, the few commercial NLG systems weare aware of are in the same situation.125 ConclusionWe have presented an operational system and, whilemany NLG prototypes exist, not many are commer-cialized, eventhough NLG technology is mature.EasyText is an instantiation of a ready-to-useframework, TextElaborator, which is based on solidscientific basis concerning not only its architecture— the standard one (Reiter and Dale, 2000) — butalso the particular instantiation of this architecturewith well-established analysis formalisms (SDRTand TAG) which have been adapted to text gener-ation.It is foreseen that TextElaborator will be used forother applications and will produce texts in otherlanguages than French, since it was developed as aready-to-use framework. For a new application, thedomain ontology has to be adapted and the G-TAGlexical databases associated with the concepts haveto be filled. When moving to another language, onlythe lexical databases will have to be changed, hope-fully.A demonstration of EasyText will be presentedduring the conference.ReferencesNicholas Asher and Alex Lascarides. 2001. Indirectspeech acts. Synthese, 128(1–2):183–228.Nicholas Asher. 1993. Reference to Abstract Objects inDiscourse. Kluwer, Dordrecht.Laurence Danlos, Bertrand Gaiffe, and Laurent Rous-sarie. 2001. Document structuring a` la SDRT. In In-ternational workshop on text generation - ACL, pages94–102, Toulouse.Laurence Danlos. 2001. G-TAG: A lexicalized for-malism for text generation inspired from TAG. In11Guy Lapalme’s personal communication in the late 90’s.12See the NLG applications developed in the American busi-ness world by Cogintex (http://www.cogentex.com)and in the French business world by Yseop (http://www.yseop.com).A. Abeille´ and O. Rambow, editors, TAG Grammar.CSLI.Laurence Danlos. 2009. D-STAG: a formalism fordiscourse analysis based on SDRT and using syn-chronous TAG. In Proceedings of the 14th Conferenceon Formal Grammar (FG’09), pages 1–20, Bordeaux,France.Claire Gardent and Eric Kow. 2007. A symbolic ap-proach to near-deterministic surface realisation usingtree adjoining grammar. In Proceedings of the 45thAnnual Meeting of the Association of ComputationalLinguistics, pages 328–335, Prague, Czech Republic,June. Association for Computational Linguistics.Aravind Joshi. 1985. Tree-adjoining grammars. InD. Dowty, L. Karttunen, and A. Zwicky, editors, Natu-ral language parsing, pages 206–250. Cambridge Uni-versity Press.Laura Kallmeyer. 2002. Using an enriched tag derivationstructure as basis for semantics. In Proceedings of theTAG+6 Workshop, pages 101–110, Venice.Fre´de´ric Meunier. 1997. Imple´mentation du formalismeG-TAG. The`se de doctorat en informatique, Universite´Denis Diderot, Paris 7.Ehud Reiter and Robert Dale. 2000. Building NaturalLanguage Generation Systems. Cambridge UniversityPress, Cambridge.Matthew Stone and Christine Doran. 1997. Sentenceplanning as description using tree adjoining grammar.In Proceedings of the 35th Annual Meeting of the As-sociation for Computational Linguistics, pages 198–205, Madrid, Spain, July. Association for Computa-tional Linguistics.Bonnie Webber. 2004. D-LTAG: extending lexicalizedTAG to discourse. Cognitive Science, 28(5):751–779.

EasyText: an Operational NLG System

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HAL Id: inria-00614760https://hal.inria.fr/inria-00614760Submitted on 16 Aug 2011HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.EasyText: an Operational NLG SystemLaurence Danlos, Frédéric Meunier, Vanessa CombetTo cite this version:Laurence Danlos, Frédéric Meunier, Vanessa Combet. EasyText: an Operational NLG System. ENLG2011 - 13th European Workshop on Natural Language Generation, Sep 2011, Nancy, France. ￿inria-00614760￿EasyText : an Operational NLG SystemLaurence DanlosUniv Paris Diderot,Sorbonne Paris Cité,ALPAGE, UMR-I 001 INRIADanlos@linguist.jussieu.frFrédéric MeunierWatchSystem AssistanceMeunier@watchsystance.comVanessa CombetWatchSystem AssistanceCombet@watchsystance.comAbstractThis paper introduces EasyText, a fully oper-ational NLG System. This application pro-cesses numerical data (in tables) in order togenerate specific analytical comments of thesetables. We start by describing the context ofthis particular NLG application (communica-tive goal, user profiles, etc.). We then shortlypresent the theoretical background which un-derlies EasyText, before describing its imple-mentation and realization.1 IntroductionEasyText is a NLG system which is operationalat Kantar Media, a French subsidiary company ofTNS-Sofres1. The company compiles numericaldata for its customers on their advertising invest-ments and sends to each customer seven tables ev-ery month, see Figure 1 for an example of a table.Before the existence of EasyText, these tables werepresented with a general commentary written by amedia analyst. Kantar decided to accompany thesetables with specific charts and comments in orderto make their reading easier. They survey 600 seg-ments and there are 7 tables per segment: Manu-ally writing these analytical comments was incon-ceivable. The idea of having an automatic systemproducing them naturally arose, but Kantar encoun-tered major difficulties with the text generation task.Therefore, they subcontracted this project to Watch1Kantar Media is one of the leaders in advertising expen-diture monitoring, exploring all existing media (radio, internet,mobile telephony, etc.).System Assistance. Figure 1 shows an example ofan analytical comment generated by EasyText.Section 2 describes the architecture of EasyText.Section 3 presents its implementation: EasyText isan instantiation for Kantar Media’s needs of a ready-to-use NLG framework, TextElaborator. Section 4gives some details on the realization and evaluationof EasyText.2 Architecture of EasyTextEasyText follows a standard architecture as de-scribed in (Reiter and Dale, 2000). It includes adocument planner for the content determination anddocument structuring tasks, and a tactical compo-nent.The content determination task for a given ta-ble amounts to detecting the relevant cells of the ta-ble. This task was guided by business rules indicatedby Kantar Media’s analysts. These rules were hard-coded, i.e. without any reasoning module.The content of a cell is transformed into the con-ceptual representation of an eventuality whose pred-icate is given by the column heading. This predicatesubcategorizes two arguments, the first one corre-sponding to the line heading, the second one to thevalue of the cell. Therefore, the output of the contentdetermination module can be seen as a conjunctionof conceptual representations of eventualities.The document structuring task consists in in-troducing rhetorical relations between the semanticcontent of the highlighted cells. For instance, if twoopposite evolutions over a given period are observed(one decreasing, the other one increasing), the rela-tion Contrast is introduced. On the contrary, wouldÉvolution des investissements par Secteur / VariétéInvestissements publicitaires plurimedia - Tri décroissant sur le cumul de l'année en cours - En k€Mai2008Mai2009Evol%Cumuljanvier à mai2008Cumuljanvier à mai2009Evol%ORGANISMES FINANCIERS 16 587 26 312 59 % 216 948 177 353 -18 %CREDIT PERSONNEL O.F 5 868 11 227 91 % 50 610 53 772 6 %~MULTIPROD.ORG.FINANCIERS 3 243 7 463 130 % 53 191 51 718 -3 %CREDIT RENOUVELABLE O.F 3 930 1 994 -49 % 60 094 34 987 -42 %INTERNET TELEMATIQUE 583 2 648 4 687 77 % 16 460 27 613 68 %RACHAT DE CREDITS O.F 777 732 -6 % 15 817 5 637 -64 %CREDIT AUTO MOTO O.F 79 110 39 % 5 638 993 -82 %CREDIT TRAVAUX O.F 86  535 797 49 %PARRAINAGE MECENAT O.F  80 0 -100 %Dans  votre  univers,  les  investissements  marquent  une  très  forte  progression  (+59%)  dans  le  secteur  ORGANISMES FINANCIERS en mai 2008 par rapport à mai 2007. Toutefois, pour le cumul à date de l'étude, ils connaissent une baisse de 18%.Dans ce secteur, les investissements ont doublé (+130%) pour la variété MULTIPROD.ORG.FINANCIERS en mai 2008 par  rapport à mai 2007. Par ailleurs, les investissements pour la variété CREDIT PERSONNEL O.F marquent une progression de  6%  pour  le  cumul  à  date  étudié.  Au  contraire,  pour  la  variété  MULTIPROD.ORG.FINANCIERS,  ils  voient  leur  volume diminuer (-3%) sur la même période.(Within your business area, ad spending ramps up (+59%) for sector ORGANISMES FINANCIERS in May 2008 compared with  May  2007.  However,  year  to  date,  it  falls  18%.  Within  this  sector,  ad  spending  doubles  (+130%)  for  segment  MULTI-PROD.ORG.FINANCIERS in May 2008 compared to May 2007. Furthermore, ad spending for segment CREDIT PERSONNEL  OF increases of 6% year to date. On the contrary, for segment MULTIPROD.ORG.FINANCIERS, it decreases (-3%) over the  same period.)Figure 1: Example of a table and its automatically generated commentthey have been going in the same direction, the re-lation Parallel would have been introduced, alongwith some hints to prepare an aggregation operationin the tactical component.The discourse theory on which the documentstructuring module relies is SDRT (Segmented Dis-course Representation Theory) (Asher, 1993; Asherand Lascarides, 2001), following (Danlos et al.,2001). The output of the document structuringcomponent is therefore consistent with a SDRS(SDRT structure), considered as a “conceptual” rep-resentation in which concepts (discourse relations,eventualities, entities) are embedded in a depen-dency structure (which is mathematically a DirectedAcyclic Graph).The tactical component (macro/micro-plannerand surface realizer) is based on G-TAG formal-ism (Danlos, 2001), the latter being itself foundedon lexicalized Tree Adjoining Grammars (TAG),(Joshi, 1985).2 G-TAG deals with the How to say it?2Since it was put forward by A. Joshi that TAG is an espe-cially well suited grammatical theory for text generation, adapt-ing TAG for generation has been widely explored, among manyothers, let us cite (Stone and Doran, 1997) and (Gardent andissue, understood as covering all (and only) linguis-tic decisions: segmentation of the text into sentencesand linear ordering of these sentences3, choice ofdiscourse connectives and other lexical items, syn-tactic constructions within sentences, aggregationoperations, referring expressions, semantic and syn-tactic parallelism, etc.The surface realizer is designed to use the syntac-tic and lexical information of a TAG grammar. ThisTAG grammar is extended to handle multi-sententialtexts and not only isolated sentences4. Therefore,the macro/mico planner is designed as a TAG exten-sion. More precisely, the architecture of G-TAG isoutlined in Figure 2:• The output of the macro/mico planner is a ”g-derivation tree”. In TAG, a derivation tree isKow, 2007). However, it is not in the scope of this paper tocompare G-TAG with these other approaches.3These tasks are not considered as part of the documentstructuring component. This is why the term macro/mico-planner is used in Figure 2.4The idea of extending TAG to handle multi-sentential textsis also used in text interpretation, e.g. D-LTAG (Webber, 2004)and D-STAG (Danlos, 2009).SDRSMacro/Micro Plannerg-derivation treeSurface Realizerg-derived treePost-processing moduleTextFigure 2: G-TAG tactical componentnot only seen as the history of the derivationbut also as a linguistic representation, close tosemantics, which can serve as a basis for adeeper semantic analysis (Kallmeyer, 2002). Ag-derivation tree in G-TAG is closer to seman-tics than a derivation tree in TAG: it is a se-mantic dependency tree annotated with syntac-tic information. Moreover, a g-derivation treerepresents a text while a derivation tree repre-sents a unique sentence.The macro/micro planner relies on lexicaldatabases associated with the various concepts(discourse relations, eventualities, entities) thatare relevant for the NLG application. A lexicaldatabase for a given element records the lex-emes lexicalizing it with their argument struc-ture, and the mappings between the conceptualand semantic arguments. With such a lexical-ized planner, the process for computing a g-derivation tree relies upon a single type of op-eration: lexicalization, i.e. choice of a lexemeand its syntactic realization to convey an in-stance of a concept. Since all the main deci-sions are made during this process, G-TAG canbe considered as a fully lexicalized formalismfor text generation.• Thanks to a TAG grammar (which specifiesthe mapping between the semantic and syntac-tic arguments), a g-derivation tree specifies aunique ”g-derived tree”, in the same way as aderivation tree specifies a unique derived tree.A g-derived tree is a syntactic tree annotatedwith morphological information.• From a g-derived tree, a post-processing mod-ule computes a text by performing morpholog-ical computations5 and formatting operations.Lexical databases for EasyText have been devel-oped by a linguist, V. Combet, who was workingin close collaboration with Kantar Media’s analysts.Particular attention was paid to linguistic variationin order to avoid producing tiresome texts for KantarMedia’s customers. This variation mainly concerns:• the lexical choices: the databases associatedto a given concept are as exhaustive as pos-sible. For example, the concept INCREASEwith a MAGNITUDE argument is lexical-ized either with the verb augmenter, doubleror tripler or with the light verb constructionêtre en hausse/augmentation or enregistrer unehausse/augmentation6. Moreover, a verb canbe modified with an adverb, e.g. faiblement,fortement, modéremment for augmenter andpresque/pratiquement/plus que for doubler ortripler7, while the noun in a light verb con-struction can be modified with a preposed ad-jective, e.g. faible/forte, or a postposed one,e.g. modérée8.• the order of the phrases: some phrases can ap-pear more or less freely in different places ina sentence. This is the case for duration ad-verbials such as pendant le mois de mai (dur-ing May) and also for different prepositionalphrases such as les investissements [pour lavariété X] augmentent [pour la variété X] (ad5Morphological operations include elisions (la augmenta-tion → l’augmentation) and contractions (de le mois → dumois).6In English, verbs increase, double or triple and light verbconstructions be on the increase or record an increase.7In English, adverbs slightly, seriously, moderately for in-crease and almost, nearly, more than for double or triple.8In English, an adjective is always preposed.spendings [for sector X] increase [for sectorX]).3 ImplementationA prototype of G-TAG was first implemented in Ada(Meunier, 1997). G-TAG has been re-implementedas a ready-to-use framework, TextElaborator. Text-Elaborator is based on the Microsoft .Net frame-work. Particular attention was paid on functionaland business issues while taking advantage of .Netfor technical and non functional issues (persistence,reliability, scalability, etc.). We chose to rely onclassical design patterns9, which garantee an effort-less reusability of the different components.Our main implementation effort for TextElabora-tor was to build an IDE (Integrated Development En-vironment) incorporating tools which facilitate thelinguistic work, i.e. feeding, editing, debugging andtesting the various lexical databases — tasks which9DAO (Data Access Object, http://en.wikipedia.org/wiki/Data_access_object) and DTO (DataTransfer Object, http://en.wikipedia.org/wiki/Data_transfer_object) .are crucial in G-TAG not only in the developmentbut also the maintenance phases.A screenshot of this IDE is shown in Figure 3.The left column gives the domain ontology hier-archized as Abstract Objects (discourse relationsand eventualities) and Entities. When clicking ona concept of the domain ontology (e.g. Haussein Figure 3), the tab LexicalPredicates in-dicates the G-TAG lexical database associated withthis concept. When choosing an element of thisdatabase, the corresponding g-derivation tree is dis-played, along with some corollary information.EasyText is an instantiation of TextElaborator forKantar Media’s needs, constiting in an ontology andits corresponding lexical databases. TextElaboratoris written in C# language and is built and run uponthe Microsoft .Net framework. Thanks to .Net, itsintegration into Kantar Media’s information systemwas easy. Generating a comment as the one shownin Figure 1 requires an average of 400ms.Figure 3: Sceenshot of TextElaborator’s integrated development environment4 Realization and EvaluationThe development of EasyText took 7 mm (menmonth) altogether:• 1 mm was dedicated to the linguist’s training toTAG and G-TAG;• 1 mm was dedicated to interviews with Kan-tar’s media analysts;• 3 mm to design TextElaborator and its IDE;• 2 mm to fill the lexical databases.During these 7 months, we were never in con-tact with Kantar’s customers directly, but workedin closed interaction with the two departments in-volved in the project. On the one hand, we obvi-ously interacted with Kantar’s media analysts. Theyshared with us all their know-how on writing com-mentaries on Kantar’s tables, enabling us to cre-ate lexical databases corresponding to their editorialhabits.On the other hand, EasyText was developed inclose collaboration with Kantar’s Information sys-tem department, so as to meet their technical re-quirements: performance and compatibility with theexisting infrastructure.When we released the first version of EasyText,Kantar decided to send the automatically generatedcommentaries to a couple of customers, without say-ing anything about the way they have been written.These customers made some critics10 but gaveKantar Media the feedback that they were quitepleased with this offer. Therefore, Kantar Media de-cided in April 2010 to commercialize this new prod-uct and acknowledged that the commentaries wereautomatically generated. They keep on commercial-izing this service, which means that their clients aresatisfied.EasyText evaluation was made by Kantar’s me-dia analysts during several months. This evaluationwas qualitative and concerned the relevance of thecommentaries (the choice of the cells to comment)and their smoothness. We remind the reader (Sec-tion 1) that EasyText commentaries had never been10The main critic concerned the laying-out of these commen-taries.handwritten. Therefore, we cannot make any com-parison between the generated texts and handwrittenones. This situation seems to be quite usual, sinceNLG applications are likely to be commercializedwhen automatic writing doesn’t replace hand writ-ing11. Indeed, the few commercial NLG systems weare aware of are in the same situation.125 ConclusionWe have presented an operational system and, whilemany NLG prototypes exist, not many are commer-cialized, eventhough NLG technology is mature.EasyText is an instantiation of a ready-to-useframework, TextElaborator, which is based on solidscientific basis concerning not only its architecture— the standard one (Reiter and Dale, 2000) — butalso the particular instantiation of this architecturewith well-established analysis formalisms (SDRTand TAG) which have been adapted to text gener-ation.It is foreseen that TextElaborator will be used forother applications and will produce texts in otherlanguages than French, since it was developed as aready-to-use framework. For a new application, thedomain ontology has to be adapted and the G-TAGlexical databases associated with the concepts haveto be filled. When moving to another language, onlythe lexical databases will have to be changed, hope-fully.A demonstration of EasyText will be presentedduring the conference.ReferencesNicholas Asher and Alex Lascarides. 2001. Indirectspeech acts. Synthese, 128(1–2):183–228.Nicholas Asher. 1993. Reference to Abstract Objects inDiscourse. Kluwer, Dordrecht.Laurence Danlos, Bertrand Gaiffe, and Laurent Rous-sarie. 2001. Document structuring à la SDRT. In In-ternational workshop on text generation - ACL, pages94–102, Toulouse.Laurence Danlos. 2001. G-TAG: A lexicalized for-malism for text generation inspired from TAG. In11Guy Lapalme’s personal communication in the late 90’s.12See the NLG applications developed in the American busi-ness world by Cogintex (http://www.cogentex.com)and in the French business world by Yseop (http://www.yseop.com).A. Abeillé and O. Rambow, editors, TAG Grammar.CSLI.Laurence Danlos. 2009. D-STAG: a formalism fordiscourse analysis based on SDRT and using syn-chronous TAG. 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EasyText: an Operational NLG System

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