An improved method for text summarization using lexical chains

This work is directed toward the creation of a system for automatically sum-marizing documents by extracting selected sentences. Several heuristics including position, cue words, and title words are used in conjunction with lexical chain in-formation to create a salience function that is used to rank sentences for extraction. Compiler technology, including the Flex and Bison tools, is used to create the AutoExtract summarizer that extracts and combines this information from the raw text. The WordNet database is used for the creation of the lexical chains. The AutoExtract summarizer performed better than the Microsoft Word97 AutoSummarize tool and the Sinope commercial summarizer in tests against ideal extracts and in tests judged by humans

Automatic Text Summarization

Writing text was one of the first ever methods used by humans to represent their knowledge. Text can be of different types and have different purposes. Due to the evolution of information systems and the Internet, the amount of textual information available has increased exponentially in a worldwide scale, and many documents tend to have a percentage of unnecessary information. Due to this event, most readers have difficulty in digesting all the extensive information contained in multiple documents, produced on a daily basis. A simple solution to the excessive irrelevant information in texts is to create summaries, in which we keep the subject’s related parts and remove the unnecessary ones. In Natural Language Processing, the goal of automatic text summarization is to create systems that process text and keep only the most important data. Since its creation several approaches have been designed to create better text summaries, which can be divided in two separate groups: extractive approaches and abstractive approaches. In the first group, the summarizers decide what text elements should be in the summary. The criteria by which they are selected is diverse. After they are selected, they are combined into the summary. In the second group, the text elements are generated from scratch. Abstractive summarizers are much more complex so they still need a lot of research, in order to represent good results. During this thesis, we have investigated the state of the art approaches, implemented our own versions and tested them in conventional datasets, like the DUC dataset. Our first approach was a frequency­based approach, since it analyses the frequency in which the text’s words/sentences appear in the text. Higher frequency words/sentences automatically receive higher scores which are then filtered with a compression rate and combined in a summary. Moving on to our second approach, we have improved the original TextRank algorithm by combining it with word embedding vectors. The goal was to represent the text’s sentences as nodes from a graph and with the help of word embeddings, determine how similar are pairs of sentences and rank them by their similarity scores. The highest ranking sentences were filtered with a compression rate and picked for the summary. In the third approach, we combined feature analysis with deep learning. By analysing certain characteristics of the text sentences, one can assign scores that represent the importance of a given sentence for the summary. With these computed values, we have created a dataset for training a deep neural network that is capable of deciding if a certain sentence must be or not in the summary. An abstractive encoder­decoder summarizer was created with the purpose of generating words related to the document subject and combining them into a summary. Finally, every single summarizer was combined into a full system. Each one of our approaches was evaluated with several evaluation metrics, such as ROUGE. We used the DUC dataset for this purpose and the results were fairly similar to the ones in the scientific community. As for our encoder­decode, we got promising results.O texto é um dos utensílios mais importantes de transmissão de ideias entre os seres humanos. Pode ser de vários tipos e o seu conteúdo pode ser mais ou menos fácil de interpretar, conforme a quantidade de informação relevante sobre o assunto principal. De forma a facilitar o processamento pelo leitor existe um mecanismo propositadamente criado para reduzir a informação irrelevante num texto, chamado sumarização de texto. Através da sumarização criam­se versões reduzidas do text original e mantém­se a informação do assunto principal. Devido à criação e evolução da Internet e outros meios de comunicação, surgiu um aumento exponencial de documentos textuais, evento denominado de sobrecarga de informação, que têm na sua maioria informação desnecessária sobre o assunto que retratam. De forma a resolver este problema global, surgiu dentro da área científica de Processamento de Linguagem Natural, a sumarização automática de texto, que permite criar sumários automáticos de qualquer tipo de texto e de qualquer lingua, através de algoritmos computacionais. Desde a sua criação, inúmeras técnicas de sumarização de texto foram idealizadas, podendo ser classificadas em dois tipos diferentes: extractivas e abstractivas. Em técnicas extractivas, são transcritos elementos do texto original, como palavras ou frases inteiras que sejam as mais ilustrativas do assunto do texto e combinadas num documento. Em técnicas abstractivas, os algoritmos geram elementos novos. Nesta dissertação pesquisaram­se, implementaram­se e combinaram­se algumas das técnicas com melhores resultados de modo a criar um sistema completo para criar sumários. Relativamente às técnicas implementadas, as primeiras três são técnicas extractivas enquanto que a ultima é abstractiva. Desta forma, a primeira incide sobre o cálculo das frequências dos elementos do texto, atribuindo­se valores às frases que sejam mais frequentes, que por sua vez são escolhidas para o sumário através de uma taxa de compressão. Outra das técnicas incide na representação dos elementos textuais sob a forma de nodos de um grafo, sendo atribuidos valores de similaridade entre os mesmos e de seguida escolhidas as frases com maiores valores através de uma taxa de compressão. Uma outra abordagem foi criada de forma a combinar um mecanismo de análise das caracteristicas do texto com métodos baseados em inteligência artificial. Nela cada frase possui um conjunto de caracteristicas que são usadas para treinar um modelo de rede neuronal. O modelo avalia e decide quais as frases que devem pertencer ao sumário e filtra as mesmas através deu uma taxa de compressão. Um sumarizador abstractivo foi criado para para gerar palavras sobre o assunto do texto e combinar num sumário. Cada um destes sumarizadores foi combinado num só sistema. Por fim, cada uma das técnicas pode ser avaliada segundo várias métricas de avaliação, como por exemplo a ROUGE. Segundo os resultados de avaliação das técnicas, com o conjunto de dados DUC, os nossos sumarizadores obtiveram resultados relativamente parecidos com os presentes na comunidade cientifica, com especial atenção para o codificador­descodificador que em certos casos apresentou resultados promissores

Text Summarization

Text summarization is the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks) [2]. By providing a text summarization system that will simplify the bulk of information and producing only the most important points, the task of reading and understanding a text would inevitably be made easier and faster. With a large volume of text documents, a summary of each document greatly facilitates the task of finding the desired documents and the desired data from the documents. As a solution for the above matter, this project objective is to simplify the texts from a previous text summarization system and further reducing the number of words in a sentence, shortening the sentences and eliminating sentences with similar meanings and also produce grammar rules that generate sentences that are human-like. The waterfall model is chosen as the project development life cycle. A detailed research has been conducted during the requirement definition phase and the system prototype is designed in the system and software design phase. During the development phase, the coding implementation will be conducted and the unit testing part will be done throughout that development process. After the entire unit has been tested, they will be integrated together and the system testing can be done as a whole. The complete program is put through thorough test and evaluation to ensure its functionality and efficiency. As the conclusion, this project should be able to produce a summarized text as the output product and meet the project requirements and objectives

An automated text summarization methodology

Most of the information is embedded in a long text documents.Having a summarizer that can produce a summary from the texts automatically is very desirable.This paper presents an introduction of an automated text summarization system by addressing the history of summarization and its existing application tools, and proposes a methodology for an automated text summarization.The proposed methodology utilized possibility and probability theory in the sentence extraction and sentence abstraction.The possibility and probability are also utilized in identifying relevant words and term occurrences techniques