Text Summarization by Sentence Extraction and Syntactic Pruning

Nous présentons une méthode hybride pour le résumé de texte, en combinant l'extraction de phrases et l'élagage syntaxique des phrases extraites. L'élagage syntaxique est effectué sur la base d’une analyse complète des phrases selon un parseur de dépendances, analyse réalisée par la grammaire développée au sein d'un logiciel commercial de correction grammaticale, le Correcteur 101. Des sous-arbres de l'analyse syntaxique sont supprimés quand ils sont identifiés par les relations ciblées. L'analyse est réalisée sur un corpus de divers textes. Le taux de réduction des phrases extraites est d’en moyenne environ 74%, tout en conservant la grammaticalité ou la lisibilité dans une proportion de plus de 64%. Étant donné ces premiers résultats sur un ensemble limité de relations syntaxiques, cela laisse entrevoir des possibilités pour une application de résumé automatique de texte.CRSN

Improving Product-related Patent Information Access with Automated Technology Ontology Extraction

Ph.DDOCTOR OF PHILOSOPH

Text summarization using concept hierarchy

This dissertation aims to create new sentences to summarize text documents. In addition to generating new sentences, this project also generates new concepts and extracts key sentences to summarize documents. This project is the first research work that can generate new key concepts and can create new sentences to summarize documents. Automatic document summarization is the process of creating a condensed version of the document. The condensed version extracts the key contents from the original document. Most related research uses statistical methods that generate a summary based on word distribution in the document. In this dissertation, we create a summary based on concept distributions and concept hierarchies. We use Stanford parser as our syntax parser and ResearchCyc (Cyc) as our knowledge base. Words and phrases of a document are mapped into Cyc concepts. We introduce a unique concept propagation method to generate abstract concepts and use those abstract concepts for the summarization. This method has two advantages over the existing methods. One advantage is the use of multi-level upward propagation to solve the word sense disambiguation problem. The other is that the propagation process provides a method to produce generalized concepts. In the first part of the project, we generate a summary by extracting key concepts and key sentences from documents. We use Stanford parser to segment a document to sentences and to parse each sentence to words or phrases tagged with their part-of-speeches. We use Cyc commands to map those words and phrases to their corresponding Cyc concepts and increase the weights of those concepts. To handle word sense disambiguation and to create summarized concepts, we propagate the weight of the concepts upward along the Cyc concept hierarchy. Then, we extract the concepts with some of the highest weights to be the key concepts. To extract key sentences from the document, we weigh each sentence in the document based on the concept weight associated with the sentence. Then, we extract the sentences with some of the highest weights to summarize the document. In the second part of the project, we generate new sentences to summarize a document based on the generalized concepts. First, we extract the subject, predicate, and object from each sentence. Then, we create compatible matrices based on the compatibility between the subjects, predicates, and objects among sentences. Two terms are considered to be compatible if the following three conditions hold: the two terms are the same concept, one concept is the other concept\u27s immediate super class, or two concepts share the same immediate super class. From the compatible matrices, we build compatible clusters and finally generate new sentences for each compatible cluster. These newly generated sentences serve as a summary for the document. We have implemented and tested our approaches. The test results show that our approaches are viable and have great potential for future research

Unsupervised extraction of semantic relations using discourse information

La compréhension du langage naturel repose souvent sur des raisonnements de sens commun, pour lesquels la connaissance de relations sémantiques, en particulier entre prédicats verbaux, peut être nécessaire. Cette thèse porte sur la problématique de l'utilisation d'une méthode distributionnelle pour extraire automatiquement les informations sémantiques nécessaires à ces inférences de sens commun. Des associations typiques entre des paires de prédicats et un ensemble de relations sémantiques (causales, temporelles, de similarité, d'opposition, partie/tout) sont extraites de grands corpus, par l'exploitation de la présence de connecteurs du discours signalant typiquement ces relations. Afin d'apprécier ces associations, nous proposons plusieurs mesures de signifiance inspirées de la littérature ainsi qu'une mesure novatrice conçue spécifiquement pour évaluer la force du lien entre les deux prédicats et la relation. La pertinence de ces mesures est évaluée par le calcul de leur corrélation avec des jugements humains, obtenus par l'annotation d'un échantillon de paires de verbes en contexte discursif. L'application de cette méthodologie sur des corpus de langue française et anglaise permet la construction d'une ressource disponible librement, Lecsie (Linked Events Collection for Semantic Information Extraction). Celle-ci est constituée de triplets: des paires de prédicats associés à une relation; à chaque triplet correspondent des scores de signifiance obtenus par nos mesures.Cette ressource permet de dériver des représentations vectorielles de paires de prédicats qui peuvent être utilisées comme traits lexico-sémantiques pour la construction de modèles pour des applications externes. Nous évaluons le potentiel de ces représentations pour plusieurs applications. Concernant l'analyse du discours, les tâches de la prédiction d'attachement entre unités du discours, ainsi que la prédiction des relations discursives spécifiques les reliant, sont explorées. En utilisant uniquement les traits provenant de notre ressource, nous obtenons des améliorations significatives pour les deux tâches, par rapport à plusieurs bases de référence, notamment des modèles utilisant d'autres types de représentations lexico-sémantiques. Nous proposons également de définir des ensembles optimaux de connecteurs mieux adaptés à des applications sur de grands corpus, en opérant une réduction de dimension dans l'espace des connecteurs, au lieu d'utiliser des groupes de connecteurs composés manuellement et correspondant à des relations prédéfinies. Une autre application prometteuse explorée dans cette thèse concerne les relations entre cadres sémantiques (semantic frames, e.g. FrameNet): la ressource peut être utilisée pour enrichir cette structure par des relations potentielles entre frames verbaux à partir des associations entre leurs verbes. Ces applications diverses démontrent les contributions prometteuses amenées par notre approche permettant l'extraction non supervisée de relations sémantiques.Natural language understanding often relies on common-sense reasoning, for which knowledge about semantic relations, especially between verbal predicates, may be required. This thesis addresses the challenge of using a distibutional method to automatically extract the necessary semantic information for common-sense inference. Typical associations between pairs of predicates and a targeted set of semantic relations (causal, temporal, similarity, opposition, part/whole) are extracted from large corpora, by exploiting the presence of discourse connectives which typically signal these semantic relations. In order to appraise these associations, we provide several significance measures inspired from the literature as well as a novel measure specifically designed to evaluate the strength of the link between the two predicates and the relation. The relevance of these measures is evaluated by computing their correlations with human judgments, based on a sample of verb pairs annotated in context. The application of this methodology to French and English corpora leads to the construction of a freely available resource, Lecsie (Linked Events Collection for Semantic Information Extraction), which consists of triples: pairs of event predicates associated with a relation; each triple is assigned significance scores based on our measures. From this resource, vector-based representations of pairs of predicates can be induced and used as lexical semantic features to build models for external applications. We assess the potential of these representations for several applications. Regarding discourse analysis, the tasks of predicting attachment of discourse units, as well as predicting the specific discourse relation linking them, are investigated. Using only features from our resource, we obtain significant improvements for both tasks in comparison to several baselines, including ones using other representations of the pairs of predicates. We also propose to define optimal sets of connectives better suited for large corpus applications by performing a dimension reduction in the space of the connectives, instead of using manually composed groups of connectives corresponding to predefined relations. Another promising application pursued in this thesis concerns relations between semantic frames (e.g. FrameNet): the resource can be used to enrich this sparse structure by providing candidate relations between verbal frames, based on associations between their verbs. These diverse applications aim to demonstrate the promising contributions provided by our approach, namely allowing the unsupervised extraction of typed semantic relations

Automated Semantic Analysis, Legal Assessment, and Summarization of Standard Form Contracts

Consumers are confronted with standard form contracts on a daily basis, for example, when shopping online, registering for online platforms, or opening bank accounts. With expected revenue of more than 343 billion Euro in 2020, e-commerce is an ever more important branch of the European economy. Accepting standard form contracts often is a prerequisite to access products or services, and consumers frequently do so without reading, let alone understanding, them. Consumer protection organizations can advise and represent consumers in such situations of power imbalance. However, with increasing demand, limited budgets, and ever more complex regulations, they struggle to provide the necessary support. This thesis investigates techniques for the automated semantic analysis, legal assessment, and summarization of standard form contracts in German and English, which can be used to support consumers and those who protect them. We focus on Terms and Conditions from the fast growing market of European e-commerce, but also show that the developed techniques can in parts be applied to other types of standard form contracts. We elicited requirements from consumers and consumer advocates to understand their needs, identified the most relevant clause topics, and analyzed the processes in consumer protection organizations concerning the handling of standard form contracts. Based on these insights, a pipeline for the automated semantic analysis, legal assessment, and summarization of standard form contracts was developed. The components of this pipeline can automatically identify and extract standard form contracts from the internet and hierarchically structure them into their individual clauses. Clause topics can be automatically identified, and relevant information can be extracted. Clauses can then be legally assessed, either using a knowledge-base we constructed or through binary classification by a transformer model. This information is then used to create summaries that are tailored to the needs of the different user groups. For each step of the pipeline, different approaches were developed and compared, from classical rule-based systems to deep learning techniques. Each approach was evaluated on German and English corpora containing more than 10,000 clauses, which were annotated as part of this thesis. The developed pipeline was prototypically implemented as part of a web-based tool to support consumer advocates in analyzing and assessing standard form contracts. The implementation was evaluated with experts from two German consumer protection organizations with questionnaires and task-based evaluations. The results of the evaluation show that our system can identify over 50 different types of clauses, which cover more than 90% of the clauses typically occurring in Terms and Conditions from online shops, with an accuracy of 0.80 to 0.84. The system can also automatically extract 21 relevant data points from these clauses with a precision of 0.91 and a recall of 0.86. On a corpus of more than 200 German clauses, the system was also able to assess the legality of clauses with an accuracy of 0.90. The expert evaluation has shown that the system is indeed able to support consumer advocates in their daily work by reducing the time they need to analyze and assess clauses in standard form contracts

Semantic chunking

Long sentences pose a challenge for natural language processing (NLP) applications. They are associated with a complex information structure leading to increased requirements for processing resources. Although the issue is present in many areas of research, there is little uniformity in the solutions used by research communities dedicated to individual NLP applications. Different aspects of the problem are addressed by different tasks, such as sentence simplification or shallow chunking. The main contribution of this thesis is the introduction of the task of semantic chunking as a general approach to reducing the cost of processing long sentences. The goal of semantic chunking is to find semantically contained fragments of a sentence representation that can be processed independently and recombined without loss of information. We anchor its principles in established concepts of semantic theory, in particular event and situation semantics. Most of the experiments in this thesis focus on semantic chunking defined on complex semantic representations in Dependency Minimal Recursion Semantics (DMRS), but we also demonstrate that the task can be performed on sentence strings. We present three chunking models: a) rule-based proof-of-concept DMRS chunking system; b) a semi-supervised sequence labelling neural model for surface semantic chunking; c) a system capable of finding semantic chunk boundaries based on the inherent structure of DMRS graphs, generalisable in the form of descriptive templates. We show how semantic chunking can be applied within a divide-and-conquer processing paradigm, using as an example the task of realization from DMRS. The application of semantic chunking yields noticeable efficiency gains without decreasing the quality of results