A topic modeling based approach to novel document automatic summarization

© 2017 Elsevier Ltd Most of existing text automatic summarization algorithms are targeted for multi-documents of relatively short length, thus difficult to be applied immediately to novel documents of structure freedom and long length. In this paper, aiming at novel documents, we propose a topic modeling based approach to extractive automatic summarization, so as to achieve a good balance among compression ratio, summarization quality and machine readability. First, based on topic modeling, we extract the candidate sentences associated with topic words from a preprocessed novel document. Second, with the goals of compression ratio and topic diversity, we design an importance evaluation function to select the most important sentences from the candidate sentences and thus generate an initial novel summary. Finally, we smooth the initial summary to overcome the semantic confusion caused by ambiguous or synonymous words, so as to improve the summary readability. We evaluate experimentally our proposed approach on a real novel dataset. The experiment results show that compared to those from other candidate algorithms, each automatic summary generated by our approach has not only a higher compression ratio, but also better summarization quality

Highlighter: automatic highlighting of electronic learning documents

Electronic textual documents are among the most popular teaching content accessible through e-learning platforms. Teachers or learners with different levels of knowledge can access the platform and highlight portions of textual content which are deemed as particularly relevant. The highlighted documents can be shared with the learning community in support of oral lessons or individual learning. However, highlights are often incomplete or unsuitable for learners with different levels of knowledge. This paper addresses the problem of predicting new highlights of partly highlighted electronic learning documents. With the goal of enriching teaching content with additional features, text classification techniques are exploited to automatically analyze portions of documents enriched with manual highlights made by users with different levels of knowledge and to generate ad hoc prediction models. Then, the generated models are applied to the remaining content to suggest highlights. To improve the quality of the learning experience, learners may explore highlights generated by models tailored to different levels of knowledge. We tested the prediction system on real and benchmark documents highlighted by domain experts and we compared the performance of various classifiers in generating highlights. The achieved results demonstrated the high accuracy of the predictions and the applicability of the proposed approach to real teaching documents

Word Associations as a Language Model for Generative and Creative Tasks

In order to analyse natural language and gain a better understanding of documents, a common approach is to produce a language model which creates a structured representation of language which could then be used further for analysis or generation. This thesis will focus on a fairly simple language model which looks at word associations which appear together in the same sentence. We will revisit a classic idea of analysing word co-occurrences statistically and propose a simple parameter-free method for extracting common word associations, i.e. associations between words that are often used in the same context (e.g., Batman and Robin). Additionally we propose a method for extracting associations which are specific to a document or a set of documents. The idea behind the method is to take into account the common word associations and highlight such word associations which co-occur in the document unexpectedly often. We will empirically show that these models can be used in practice at least for three tasks: generation of creative combinations of related words, document summarization, and creating poetry. First the common word association language model is used for solving tests of creativity -- the Remote Associates test. Then observations of the properties of the model are used further to generate creative combinations of words -- sets of words which are mutually not related, but do share a common related concept. Document summarization is a task where a system has to produce a short summary of the text with a limited number of words. In this thesis, we will propose a method which will utilise the document-specific associations and basic graph algorithms to produce summaries which give competitive performance on various languages. Also, the document-specific associations are used in order to produce poetry which is related to a certain document or a set of documents. The idea is to use documents as inspiration for generating poems which could potentially be used as commentary to news stories. Empirical results indicate that both, the common and the document-specific associations, can be used effectively for different applications. This provides us with a simple language model which could be used for different languages.Kielimalleja käytetään usein luonnollisten kielten ja dokumenttien ymmärtämiseen. Kielimalli on kielen rakenteellinen esitysmuoto, jota voidaan käyttää kielen analyysiin tai sen tuottamiseen. Tässä työssä esitetään yksinkertainen kielimalli, joka perustuu assosiaatioihin sanojen välillä, jotka esiintyvät samassa lausessa. Ensin tutustumme klassiseen menetelmään analysoida sanojen yhteisesiintymiä tilastollisesti, jonka perusteella esittelemme parametri-vapaan menetelmän tuottaa yleisiä sana-assosiaatioita. Nämä sana-assosiaatiot ovat yhteyksiä sellaisten sanojen välillä, jotka esiintyvät samoissa asiayhteyksissä, kuten esimerkiksi Batman ja Robin. Lisäksi esittelemme menetelmän, joka tuottaa näitä assosiaatioita tietylle dokumentille tai joukolle dokumentteja. Menetelmä perustuu niiden sana-assosiaatioiden huomioimiseen, jotka ovat lähde-dokumenteissa erityisen yleisiä. Näytämme empiirisesti, että kielimallejamme voidaan käyttää ainakin kolmeen tarkoitukseen: luovien sanayhdistelmien tuottamiseen, dokumenttien referointiin ja runojen tuottamiseen. Ratkomme ensin yleisiin sana-assosiaatioihin perustuvalla mallillamme luovuutta testaavia Remote Associates -kokeita. Sen jälkeen tuotamme mallista tehtyjen havaintojen perusteella luovia sanayhdistelmiä. Nämä yhdistelmät sisältävät sanoja, jotka eivät välttämättä ole keskenään toisiinsa liittyviä, mutta ne jakavat joitakin yhdistäviä käsitteitä. Dokumentin referointi viittaa tehtävään, jossa pitää tuottaa rajoitetun pituinen lyhennelmä pidemmästä dokumentista. Esitämme menetelmän joka tuottaa eri kielillä tasoltaan kilpailukykyisiä referaatteja, käyttäen dokumenttikohtaisia sana-assosiaatioita sekä yksinkertaisia graafi-algoritmeja. Assosiaatioiden avulla voidaan tuottaa myös dokementtikohtaisia runoja. Dokumenttien inspiroimia runoja voitaisiin käyttää esimerkiksi uutisartikkeleiden kommentointiin. Tuloksemme niin yleisiin kuin dokumenttikohtaisiin assosiaatioihin perustuvista malleista osoittavat, että näitä malleja voidaan käyttää tehokkaasti eri käyttötarkoituksiin. Tuloksena on yksinkertainen kielimalli, jota voidaan käyttää useiden eri kielten kanssa