Automatic text summarization can efficiently and effectively save users¡¦ time while reading text documents. The objective of automatic text summarization is to extract essential sentences that cover almost all the concepts of a document so that users are able to comprehend the ideas the document tries to address by simply reading through the corresponding summary. This research focuses on developing a hybrid automatic text summarization approach, KCS, to enhancing the quality of summaries. This approach basically consists of two major components: first, it employs the K-mixture probabilistic model to calculate term weights in a statistical sense; it then identifies the term relationship between nouns and nouns as well as nouns and verbs, which results in the connective strength (CS) of nouns. With the connective strengths available scores of sentences can be calculated and ranked to be extracted. We conduct three experiments to justify the proposed approach. The quality of summary is examined by its capability of increasing accuracy of text classification,while the classifier employed, the Naïve Bayes classifier, is kept the same through all experiments. The results show that the K-mixture model is more contributive to document classification than traditional TFIDF weighting scheme. It, however, is still no better than CS, a more complex linguistic-based approach. More importantly, our proposed approach, KCS, performs best among all approaches considered. It implies that KCS can extract more representative sentences from the document and its feasibility in text summarization applications is thus justified.

With an increasing amount of textual information available there is also an increased need to make this information more accessible. Our paper describes a modified TextRank model and investigates the different methods available to use automatic text summarization as a means for summary creation of swedish news articles. To evaluate our model we focused on intrinsic evaluation methods, in part through content evaluation in the form of of measuring referential clarity and non-redundancy, and in part by text quality evaluation measures, in the form of keyword retention and ROUGE evaluation. The results acquired indicate that stemming and improved stop word capabilities can have a positive effect on the ROUGE scores. The addition of redundancy checks also seems to have a positive effect on avoiding repetition of information. Keyword retention decreased somewhat, however. Lastly all methods had some trouble with dangling anaphora, showing a need for further work within anaphora resolution.

Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2018-04-19T18:44:37Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2018-04-23T16:27:14Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) / Made available in DSpace on 2018-04-23T16:27:14Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) Previous issue date: 2017 / Considerando a grande quantidade de informa??es textuais dispon?veis atualmente, principalmente na web, est? se tronando cada vez mais dif?cil o acesso e a assimila??o desse conte?do para o usu?rio. Nesse contexto, torna-se necess?rio buscar tarefas capazes de transformar essa grande quantidade de dados em conhecimento ?til e organizado. Uma alternativa para amenizar esse problema, ? reduzir o volume de informa??es dispon?veis a partir da produ??o de resumos dos textos originais, por meio da sumariza??o autom?tica (SA) de textos. A sumariza??o autom?tica de textos consiste na produ??o autom?tica de resumos a partir de um ou mais textos-fonte, de modo que o sum?rio contenha as informa??es mais relevantes deste. A avalia??o de resumos ? uma tarefa importante no campo da sumariza??o autom?tica de texto, a abordagem mais intuitiva ? a avalia??o humana, por?m ? onerosa e improdutiva. Outra alternativa ? a avalia??o autom?tica, alguns avaliadores foram propostos, sendo a mais conhecida e amplamente usada ? a medida ROUGE (Recall-Oriented Understudy for Gisting Evaluation). Um fator limitante na avalia??o da ROUGE ? a utiliza??o do sum?rio humano de refer?ncia, o que implica em uma restri??o do idioma e dom?nio, al?m de requerer um trabalho humano demorado e oneroso. Diante das dificuldades encontradas na avalia??o de sum?rios autom?ticos, o presente trabalho apresenta o modelo Cassiopeia como um novo m?todo de avalia??o. O modelo ? um agrupador de textos hier?rquico, o qual consiste no uso da sumariza??o na etapa do pr?-processamento, onde a qualidade do agrupamento ? influenciada positivamente conforme a qualidade da sumariza??o. As simula??es realizadas neste trabalho mostraram que a avalia??o realizada pelo modelo Cassiopeia ? semelhante a avalia??o realizada pela ferramenta ROUGE. Por outro lado, a utiliza??o do modelo Cassiopeia como avaliador de sum?rios autom?ticos evidenciou algumas vantagens, sendo as principais; a n?o utiliza??o do sum?rio humano no processo de avalia??o, e a independ?ncia do dom?nio e do idioma. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / Considering the large amount of textual information currently available, especially on the web, it is becoming increasingly difficult to access and assimilate this content to the user. In this context, it becomes necessary to search for tasks that can transform this large amount of information into useful and organized knowledge. The solution, or at least an alternative, to moderate this problem is to reduce the volume of information available, from the production of abstracts of the original texts, through automatic summarization (SA) of texts. The Automatic Summarization of texts consists of the automatic production of abstracts from one or more source texts, which the summary must contain the most relevant information of the source text. The evaluation of abstracts is an important task in the field of automatic text summarization, the most intuitive approach is human evaluation, but it is costly and unproductive. Another alternative is the automatic evaluation, some evaluators have been proposed, and the most widely used is the ROUGE (Recall-Oriented Understudy for Gisting Evaluation). A limiting factor in ROUGE's evaluation is the use of the human reference summary, which implies a restriction of language and domain, as well as requiring time-consuming and expensive human work. In view of the difficulties encountered in the evaluation of automatic summaries, this paper presents the Cassiopeia model as a new evaluation method. The model is a hierarchical text grouper, which consists of the use of the summarization in the stage of the pre-processing, where the quality of the grouping is influenced positively according to the quality of the summarization. The simulations performed in this work showed that the evaluations performed by Cassiopeia in comparison to the ROUGE tool are similar. On the other hand, the use of the Cassiopeia model as an automatic summarization evaluator showed some advantages, the main ones are; being the non-use of the human abstract in the evaluation process, and the independent of the domain and the language.

Automatic text summarization is a growing field due to the modern world’s Internet based society, but to automatically create perfect summaries is not easy, and cohesion errors are common. By the usage of an eye tracking camera, this thesis studies the nature of four different types of cohesion errors occurring in summaries. A total of 23 participants read and rated four different texts and marked the most difficult areas of each text. Statistical analysis of the data revealed that absent cohesion or context and broken anaphoric reference (pronouns) caused some disturbance in reading, but that the impact is restricted to the effort to read rather than the comprehension of the text. Erroneous anaphoric reference (pronouns) was not detected by the participants which poses a problem for automatic text summarizers, and other potential disturbing factors were detected. Finally, the question of the meaningfulness of keeping absent cohesion or context as a separate error type was raised.

Social media is a significant factor in information distribution today, and this information landscape contains a lot of different posts that compete for the user’s attention. Different factors can help catch the interest of the user, and one of them is the headline of the message. The headline can be more or less eye-catching, which can make the reader more or less interested in interacting with the post. The theme of this study is the automatized creation of eye-catching headlines that stay truthful to the content of the articles using Automatic Text Summarization. The exact method used consisted of fine-tuning the BART model, which is an existing model for Text Summarization. Other papers have been written using different models to solve this problem with more or less success, however, none have used this method. It was deemed an interesting method as it is less time- and energy-consuming than creating and training a new model entirely from scratch and therefore could be easily replicated if the results were positive. The BartForConditionalGeneration model implemented by the HuggingFace library was fine-tuned, using the Popular News Articles by Web.io. This method showed positive results. The resulting headlines were deemed faithful to the original ones, with a ROUGE-2 recall score of 0.541. They were comparably eye-catching to the human-written headlines, with the human respondents ranking them almost the same, with an average rank of 1.692 for the human-written headlines, and 1.821 for fine-tuned BART, and also getting an average score of 3.31 on a 1 to 5 attractiveness score scale. They were also deemed very comprehensible, with an average score of 0.95 on a scale from 0 to 1. / Sociala medier är idag en viktig faktor i distributionen av information. Detta nya landskap innehåller många olika inlägg som tävlar om användarens uppmärksamhet. Olika faktorer kan hjälpa till att fånga användarens blick till specifika inlägg eller artiklar, och en av dessa faktorer är rubriken. Rubriken kan vara mer eller mindre fängslande, och göra läsaren mer eller mindre intresserad av att interagera med inlägget. Temat för denna studie är att automatiskt skapa iögonfallande och intressanta rubriker, som beskriver innehå llet i artiklarna på ett korrekt sätt. Den valda metoden är automatisk textsamman fattning, och mer specifikt finjusterades BART-modellen, som är en existerande modell för textsammanfattning. Andra metoder har använts tidigare för att lösa denna problematik med mer eller mindre framgång, men ingen studie hade använt den här. Den ansågs vara intressant eftersom den är mindre tids- och energikrävande än vad det skulle vara att skapa en ny modell från grunden, och därför skulle den lätt kunna replikeras om resultatet var positivt. BartForConditionalGeneration-modellen implementerad av HuggingFace-bib lioteket finjusterades därför med hjälp av artiklar och rubriker från datasetet ’Popular News Articles’ av Web.io. Metoden visade positiva resultat. De resulterande rubrikerna ansågs trogna de ursprungliga, med en ROUGE-2 recall score på 0,541. De var jämförbart iögonfallande gentemot de mänskligt skrivna rubrikerna, då respondenterna rankade dem nästan likadant, med en genomsnittlig rankning på 1,692 för de mänskligt skrivna rubrikerna och 1,821 för rubrikerna som finjusterade BART genererade. De fick också ett genomsnittligt betyg av 3,31 på en poängskala från 1 till 5. De ansågs dessutom vara mycket lättbegripliga, med ett medelpoäng på 0,95 på en skala från 0 till 1.

Automatic text summarization has emerged as a promising solution to manage the vast amount of information available on the internet, enabling a wider audience to access it. Nevertheless, further development and experimentation with different approaches are still needed. This thesis explores the potential of combining extractive and abstractive approaches into a hybrid method, generating three types of summaries: extractive, abstractive, and hybrid. The news articles used in the study are from the Swedish newspaper Dagens Nyheter(DN). The quality of the summaries is assessed using various automatic measures, including ROUGE, BERTScore, and Coh-Metrix. Additionally, human evaluations are conducted to compare the different types of summaries in terms of perceived fluency, adequacy, and simplicity. The results of the human evaluation show a statistically significant difference between attractive, abstractive, and hybrid summaries with regard to fluency, adequacy, and simplicity. Specifically, there is a significant difference between abstractive and hybrid summaries in terms of fluency and simplicity, but not in adequacy. The automatic measures, however, do not show significant differences between the different summaries but tend to give higher scores to the hybrid and abstractive summaries

Developments in deep learning and machine learning overall has created a plethora of opportunities for easier training of automatic text summarization (ATS) models for producing summaries with higher quality. ATS can be split into extractive and abstractive tasks; extractive models extract sentences from the original text to create summaries. On the contrary, abstractive models generate novel sentences to create summaries. While extractive summaries are often preferred over abstractive ones, summaries created by extractive models trained on Swedish texts often lack cohesion, which affects the readability and overall quality of the summary. Therefore, there is a need to improve the process of training ATS models in terms of cohesion, while maintaining other text qualities such as content coverage. This thesis explores and implements methods at the data-processing stage aimed at improving cohesion of generated summaries. The methods are based around Sentence-BERT for creating advanced sentence embeddings that can be used to rank sentences in a text in terms of if it should be included in the extractive summary or not. Three models are trained using different methods and evaluated using ROUGE, BERTScore for measuring content coverage and Coh-Metrix for measuring cohesion. The results of the evaluation suggest that the methods can indeed be used to create more cohesive summaries, although content coverage was reduced, which gives rise to the potential for extensive future exploration of further implementation.

隨著網路世界的興起，在面臨選擇難題時，民眾不僅會接收口耳相傳的資訊，也會以關鍵字上網搜尋目標資訊，但是在海量資料的浪潮中，如何快速的整合資料是一大挑戰。電影影評文章摘要可以幫助民眾進電影院前了解電影的資訊，透過這樣的方式確認電影是自身有興趣的電影。 本研究以電影：復仇者聯盟2影評66篇4616句、蝙蝠俠對超人：正義曙光60篇9345句、動物方城市60篇5545句、星際效應50篇4616句、高年級實習生62篇5622句為資料來源，以分群概念結合摘句之方法生成影評摘要。其中，利用K-Means演算法將五部電影的多篇影評特徵詞、句子進行分群後，使用TFIDF評比各分群語句的重要性來選取高權重語句，再以WWA方法挑選分群中不同面向的語句，最後以相似度計算最佳範本與各分群內容的相似度來決定每一群聚的排序順序，產生一篇具有相似內容段落和段落順序的影評多篇摘要。 研究結果顯示，原本五部電影影評對最佳範本之相似度為15.87%，經由本研究方法產生之摘要對最佳範本單篇摘要之相似度為21.19%。另外，因為影評中各分群的順序是比對最佳範本相似度而產生的排序，整篇摘要會具有與最佳範本相似段落排序的摘要內容，其中內容包含了電影影評中廣泛提到的相似內容，不同的相似段落讓文章摘要的呈現更具廣泛性。藉由此摘要方法，可以幫助民眾藉由自動化彙整、萃取的摘要快速了解相關電影資訊內容和協助決策。 / Abstract As Facing the Big Data issue, there are too many information on the website for reader to understand. How to perform and summarize essential information quickly is a challenge. People who want to go to a movie will also face this situation. Before choosing movies, they will search relative information of the movies. However, there are many film reviews all over the websites. Automatic text summarization can efficiently extract important information for readers, and conclude concepts of reviews on the websites. Through this method, readers can easily comprehend the best idea of all the reviews and save their time. The research presents a multi-concept and extractive film review summary for readers. It generates film review summary from the most popular blog platform, PIXNET, with extract-based method and clustering concept. The method using K-Means algorism let the film review summary focus on specific film to cluster the sentences by features, and having statistical sense and WWA method to measure the weight of sentences in order to choose the representative sentences. On the last step, it will compare to templates to decide the sequence of classified sentences and summary all represent sentences from each cluster. The research provides a multi-concept and extractive film review summary for people. From the result, there are five movies, which are used summary method increase the average similarity to 21.19% that comparing between the film reviews summary and templates summary. It shows that the automatic film reviews summarization can extract the important sentences from the reviews. Also, with comparing template method to order the cluster, it can sequentially list the cluster of the sentences to generate a movie review, which saves readers’ time and easily comprehend.

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