Semantic based Text Summarization for Single Document on Android Mobile Device

The explosion of information in the World Wide Web is overwhelming readers with limitless information. Large internet articles or journals are often cumbersome to read as well as comprehend. More often than not, readers are immersed in a pool of information with limited time to assimilate all of the articles. It leads to information overload whereby readers are trying to deal with more information than they can process. Hence, there is an apparent need for an automatic text summarizer as to produce summaries quicker than humans. The text summarization research on mobile platform has been inspired by the new paradigm shift in accessing information ubiquitously at anytime and anywhere on Smartphones or smart devices. In this research, a semantic and syntactic based summarization is implemented in a text summarizer to solve the overload problem whilst providing a more coherent summary. Additionally, WordNet is used as the lexical database to semantically extract the text document which provides a more efficient and accurate algorithm than the existing summary system. The objective of the paper is to integrate WordNet into the proposed system called TextSumIt which condenses lengthy documents into shorter summarized text that gives a higher readability to Android mobile users. The experimental results are done using recall, precision and F-Score to evaluate on the summary output, in comparison with the existing automated summarizer. Human-generated summaries from Document Understanding Conference (DUC) are taken as the reference summaries for the evaluation. The evaluation of experimental results shows satisfactory results

Semantic based Text Summarization for Single Document on Android Mobile Device

The explosion of information in the World Wide Web is overwhelming readers with limitless information. Large internet articles or journals are often cumbersome to read as well as comprehend. More often than not, readers are immersed in a pool of information with limited time to assimilate all of the articles. It leads to information overload whereby readers are trying to deal with more information than they can process. Hence, there is an apparent need for an automatic text summarizer as to produce summaries quicker than humans. The text summarization research on mobile platform has been inspired by the new paradigm shift in accessing information ubiquitously at anytime and anywhere on Smartphones or smart devices. In this research, a semantic and syntactic based summarization is implemented in a text summarizer to solve the overload problem whilst providing a more coherent summary. Additionally, WordNet is used as the lexical database to semantically extract the text document which provides a more efficient and accurate algorithm than the existing summary system. The objective of the paper is to integrate WordNet into the proposed system called TextSumIt which condenses lengthy documents into shorter summarized text that gives a higher readability to Android mobile users. The experimental results are done using recall, precision and F-Score to evaluate on the summary output, in comparison with the existing automated summarizer. Human-generated summaries from Document Understanding Conference (DUC) are taken as the reference summaries for the evaluation. The evaluation of experimental results shows satisfactory results

Beyond Extractive: Advancing Abstractive Automatic Text Summarization in Norwegian with Transformers

Automatic summarization is a key area in natural language processing (NLP) and machine learning which attempts to generate informative summaries of articles and documents. Despite its evolution since the 1950s, research on automatically summarising Norwegian text has remained relatively underdeveloped. Though there have been some strides made in extractive systems, which generate summaries by selecting and condensing key phrases directly from the source material, the field of abstractive summarization remains unexplored for the Norwegian language. Abstractive summarization is distinct as it generates summaries incorporating new words and phrases not present in the original text. This Master's thesis revolves around one key question: Is it possible to create a machine learning system capable of performing abstractive summarization in Norwegian? To answer this question, we generate and release the first two Norwegian datasets for creating and evaluating Norwegian summarization models. One of these datasets is a web scrape of Store Norske Leksikon (SNL), and the other is a machine-translated version of CNN/Daily Mail. Using these datasets, we fine-tune two Norwegian T5 language models with 580M and 1.2B parameters to create summaries. To assess the quality of the models, we employed both automatic ROUGE scores and human evaluations on the generated summaries. In an effort to better understand the model's behaviour, we measure how a model generates summaries with various metrics, including our own novel contribution which we name "Match Ratio" which measures sentence similarities between summaries and articles based on Levenshtein distances. The top-performing models achieved ROUGE-1 scores of 35.07 and 34.02 on SNL and CNN/DM, respectively. In terms of human evaluation, the best model yielded an average score of 3.96/5.00 for SNL and 4.64/5.00 for CNN/Daily Mail across various criteria. Based on these results, we conclude that it is possible to perform abstractive summarization of Norwegian with high-quality summaries. With this research, we have laid a foundation that hopefully will facilitate future research, empowering others to build upon our findings and contribute further to the development of Norwegian summarization models

Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization

[ES] Hoy en día, la sociedad tiene acceso y posibilidad de contribuir a grandes cantidades de contenidos presentes en Internet, como redes sociales, periódicos online, foros, blogs o plataformas de contenido multimedia. Todo este tipo de medios han tenido, durante los últimos años, un impacto abrumador en el día a día de individuos y organizaciones, siendo actualmente medios predominantes para compartir, debatir y analizar contenidos online. Por este motivo, resulta de interés trabajar sobre este tipo de plataformas, desde diferentes puntos de vista, bajo el paraguas del Procesamiento del Lenguaje Natural. En esta tesis nos centramos en dos áreas amplias dentro de este campo, aplicadas al análisis de contenido en línea: análisis de texto en redes sociales y resumen automático. En paralelo, las redes neuronales también son un tema central de esta tesis, donde toda la experimentación se ha realizado utilizando enfoques de aprendizaje profundo, principalmente basados en mecanismos de atención. Además, trabajamos mayoritariamente con el idioma español, por ser un idioma poco explorado y de gran interés para los proyectos de investigación en los que participamos. Por un lado, para el análisis de texto en redes sociales, nos enfocamos en tareas de análisis afectivo, incluyendo análisis de sentimientos y detección de emociones, junto con el análisis de la ironía. En este sentido, se presenta un enfoque basado en Transformer Encoders, que consiste en contextualizar \textit{word embeddings} pre-entrenados con tweets en español, para abordar tareas de análisis de sentimiento y detección de ironía. También proponemos el uso de métricas de evaluación como funciones de pérdida, con el fin de entrenar redes neuronales, para reducir el impacto del desequilibrio de clases en tareas \textit{multi-class} y \textit{multi-label} de detección de emociones. Adicionalmente, se presenta una especialización de BERT tanto para el idioma español como para el dominio de Twitter, que tiene en cuenta la coherencia entre tweets en conversaciones de Twitter. El desempeño de todos estos enfoques ha sido probado con diferentes corpus, a partir de varios \textit{benchmarks} de referencia, mostrando resultados muy competitivos en todas las tareas abordadas. Por otro lado, nos centramos en el resumen extractivo de artículos periodísticos y de programas televisivos de debate. Con respecto al resumen de artículos, se presenta un marco teórico para el resumen extractivo, basado en redes jerárquicas siamesas con mecanismos de atención. También presentamos dos instancias de este marco: \textit{Siamese Hierarchical Attention Networks} y \textit{Siamese Hierarchical Transformer Encoders}. Estos sistemas han sido evaluados en los corpora CNN/DailyMail y NewsRoom, obteniendo resultados competitivos en comparación con otros enfoques extractivos coetáneos. Con respecto a los programas de debate, se ha propuesto una tarea que consiste en resumir las intervenciones transcritas de los ponentes, sobre un tema determinado, en el programa "La Noche en 24 Horas". Además, se propone un corpus de artículos periodísticos, recogidos de varios periódicos españoles en línea, con el fin de estudiar la transferibilidad de los enfoques propuestos, entre artículos e intervenciones de los participantes en los debates. Este enfoque muestra mejores resultados que otras técnicas extractivas, junto con una transferibilidad de dominio muy prometedora.[CA] Avui en dia, la societat té accés i possibilitat de contribuir a grans quantitats de continguts presents a Internet, com xarxes socials, diaris online, fòrums, blocs o plataformes de contingut multimèdia. Tot aquest tipus de mitjans han tingut, durant els darrers anys, un impacte aclaparador en el dia a dia d'individus i organitzacions, sent actualment mitjans predominants per compartir, debatre i analitzar continguts en línia. Per aquest motiu, resulta d'interès treballar sobre aquest tipus de plataformes, des de diferents punts de vista, sota el paraigua de l'Processament de el Llenguatge Natural. En aquesta tesi ens centrem en dues àrees àmplies dins d'aquest camp, aplicades a l'anàlisi de contingut en línia: anàlisi de text en xarxes socials i resum automàtic. En paral·lel, les xarxes neuronals també són un tema central d'aquesta tesi, on tota l'experimentació s'ha realitzat utilitzant enfocaments d'aprenentatge profund, principalment basats en mecanismes d'atenció. A més, treballem majoritàriament amb l'idioma espanyol, per ser un idioma poc explorat i de gran interès per als projectes de recerca en els que participem. D'una banda, per a l'anàlisi de text en xarxes socials, ens enfoquem en tasques d'anàlisi afectiu, incloent anàlisi de sentiments i detecció d'emocions, juntament amb l'anàlisi de la ironia. En aquest sentit, es presenta una aproximació basada en Transformer Encoders, que consisteix en contextualitzar \textit{word embeddings} pre-entrenats amb tweets en espanyol, per abordar tasques d'anàlisi de sentiment i detecció d'ironia. També proposem l'ús de mètriques d'avaluació com a funcions de pèrdua, per tal d'entrenar xarxes neuronals, per reduir l'impacte de l'desequilibri de classes en tasques \textit{multi-class} i \textit{multi-label} de detecció d'emocions. Addicionalment, es presenta una especialització de BERT tant per l'idioma espanyol com per al domini de Twitter, que té en compte la coherència entre tweets en converses de Twitter. El comportament de tots aquests enfocaments s'ha provat amb diferents corpus, a partir de diversos \textit{benchmarks} de referència, mostrant resultats molt competitius en totes les tasques abordades. D'altra banda, ens centrem en el resum extractiu d'articles periodístics i de programes televisius de debat. Pel que fa a l'resum d'articles, es presenta un marc teòric per al resum extractiu, basat en xarxes jeràrquiques siameses amb mecanismes d'atenció. També presentem dues instàncies d'aquest marc: \textit{Siamese Hierarchical Attention Networks} i \textit{Siamese Hierarchical Transformer Encoders}. Aquests sistemes s'han avaluat en els corpora CNN/DailyMail i Newsroom, obtenint resultats competitius en comparació amb altres enfocaments extractius coetanis. Pel que fa als programes de debat, s'ha proposat una tasca que consisteix a resumir les intervencions transcrites dels ponents, sobre un tema determinat, al programa "La Noche en 24 Horas". A més, es proposa un corpus d'articles periodístics, recollits de diversos diaris espanyols en línia, per tal d'estudiar la transferibilitat dels enfocaments proposats, entre articles i intervencions dels participants en els debats. Aquesta aproximació mostra millors resultats que altres tècniques extractives, juntament amb una transferibilitat de domini molt prometedora.[EN] Nowadays, society has access, and the possibility to contribute, to large amounts of the content present on the internet, such as social networks, online newspapers, forums, blogs, or multimedia content platforms. These platforms have had, during the last years, an overwhelming impact on the daily life of individuals and organizations, becoming the predominant ways for sharing, discussing, and analyzing online content. Therefore, it is very interesting to work with these platforms, from different points of view, under the umbrella of Natural Language Processing. In this thesis, we focus on two broad areas inside this field, applied to analyze online content: text analytics in social media and automatic summarization. Neural networks are also a central topic in this thesis, where all the experimentation has been performed by using deep learning approaches, mainly based on attention mechanisms. Besides, we mostly work with the Spanish language, due to it is an interesting and underexplored language with a great interest in the research projects we participated in. On the one hand, for text analytics in social media, we focused on affective analysis tasks, including sentiment analysis and emotion detection, along with the analysis of the irony. In this regard, an approach based on Transformer Encoders, based on contextualizing pretrained Spanish word embeddings from Twitter, to address sentiment analysis and irony detection tasks, is presented. We also propose the use of evaluation metrics as loss functions, in order to train neural networks for reducing the impact of the class imbalance in multi-class and multi-label emotion detection tasks. Additionally, a specialization of BERT both for the Spanish language and the Twitter domain, that takes into account inter-sentence coherence in Twitter conversation flows, is presented. The performance of all these approaches has been tested with different corpora, from several reference evaluation benchmarks, showing very competitive results in all the tasks addressed. On the other hand, we focused on extractive summarization of news articles and TV talk shows. Regarding the summarization of news articles, a theoretical framework for extractive summarization, based on siamese hierarchical networks with attention mechanisms, is presented. Also, we present two instantiations of this framework: Siamese Hierarchical Attention Networks and Siamese Hierarchical Transformer Encoders. These systems were evaluated on the CNN/DailyMail and the NewsRoom corpora, obtaining competitive results in comparison to other contemporary extractive approaches. Concerning the TV talk shows, we proposed a text summarization task, for summarizing the transcribed interventions of the speakers, about a given topic, in the Spanish TV talk shows of the ``La Noche en 24 Horas" program. In addition, a corpus of news articles, collected from several Spanish online newspapers, is proposed, in order to study the domain transferability of siamese hierarchical approaches, between news articles and interventions of debate participants. This approach shows better results than other extractive techniques, along with a very promising domain transferability.González Barba, JÁ. (2021). Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172245TESI