A Review On Automatic Text Summarization Approaches

It has been more than 50 years since the initial investigation on automatic text summarization was started.Various techniques have been successfully used to extract the important contents from text document to represent document summary.In this study,we review some of the studies that have been conducted in this still-developing research area.It covers the basics of text summarization,the types of summarization,the methods that have been used and some areas in which text summarization has been applied.Furthermore,this paper also reviews the significant efforts which have been put in studies concerning sentence extraction,domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it.In addition,the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study

Document analysis by means of data mining techniques

The huge amount of textual data produced everyday by scientists, journalists and Web users, allows investigating many different aspects of information stored in the published documents. Data mining and information retrieval techniques are exploited to manage and extract information from huge amount of unstructured textual data. Text mining also known as text data mining is the processing of extracting high quality information (focusing relevance, novelty and interestingness) from text by identifying patterns etc. Text mining typically involves the process of structuring input text by means of parsing and other linguistic features or sometimes by removing extra data and then finding patterns from structured data. Patterns are then evaluated at last and interpretation of output is performed to accomplish the desired task. Recently, text mining has got attention in several fields such as in security (involves analysis of Internet news), for commercial (for search and indexing purposes) and in academic departments (such as answering query). Beyond searching the documents consisting the words given in a user query, text mining may provide direct answer to user by semantic web for content based (content meaning and its context). It can also act as intelligence analyst and can also be used in some email spam filters for filtering out unwanted material. Text mining usually includes tasks such as clustering, categorization, sentiment analysis, entity recognition, entity relation modeling and document summarization. In particular, summarization approaches are suitable for identifying relevant sentences that describe the main concepts presented in a document dataset. Furthermore, the knowledge existed in the most informative sentences can be employed to improve the understanding of user and/or community interests. Different approaches have been proposed to extract summaries from unstructured text documents. Some of them are based on the statistical analysis of linguistic features by means of supervised machine learning or data mining methods, such as Hidden Markov models, neural networks and Naive Bayes methods. An appealing research field is the extraction of summaries tailored to the major user interests. In this context, the problem of extracting useful information according to domain knowledge related to the user interests is a challenging task. The main topics have been to study and design of novel data representations and data mining algorithms useful for managing and extracting knowledge from unstructured documents. This thesis describes an effort to investigate the application of data mining approaches, firmly established in the subject of transactional data (e.g., frequent itemset mining), to textual documents. Frequent itemset mining is a widely exploratory technique to discover hidden correlations that frequently occur in the source data. Although its application to transactional data is well-established, the usage of frequent itemsets in textual document summarization has never been investigated so far. A work is carried on exploiting frequent itemsets for the purpose of multi-document summarization so a novel multi-document summarizer, namely ItemSum (Itemset-based Summarizer) is presented, that is based on an itemset-based model, i.e., a framework comprise of frequent itemsets, taken out from the document collection. Highly representative and not redundant sentences are selected for generating summary by considering both sentence coverage, with respect to a sentence relevance score, based on tf-idf statistics, and a concise and highly informative itemset-based model. To evaluate the ItemSum performance a suite of experiments on a collection of news articles has been performed. Obtained results show that ItemSum significantly outperforms mostly used previous summarizers in terms of precision, recall, and F-measure. We also validated our approach against a large number of approaches on the DUC’04 document collection. Performance comparisons, in terms of precision, recall, and F-measure, have been performed by means of the ROUGE toolkit. In most cases, ItemSum significantly outperforms the considered competitors. Furthermore, the impact of both the main algorithm parameters and the adopted model coverage strategy on the summarization performance are investigated as well. In some cases, the soundness and readability of the generated summaries are unsatisfactory, because the summaries do not cover in an effective way all the semantically relevant data facets. A step beyond towards the generation of more accurate summaries has been made by semantics-based summarizers. Such approaches combine the use of general-purpose summarization strategies with ad-hoc linguistic analysis. The key idea is to also consider the semantics behind the document content to overcome the limitations of general-purpose strategies in differentiating between sentences based on their actual meaning and context. Most of the previously proposed approaches perform the semantics-based analysis as a preprocessing step that precedes the main summarization process. Therefore, the generated summaries could not entirely reflect the actual meaning and context of the key document sentences. In contrast, we aim at tightly integrating the ontology-based document analysis into the summarization process in order to take the semantic meaning of the document content into account during the sentence evaluation and selection processes. With this in mind, we propose a new multi-document summarizer, namely Yago-based Summarizer, that integrates an established ontology-based entity recognition and disambiguation step. Named Entity Recognition from Yago ontology is being used for the task of text summarization. The Named Entity Recognition (NER) task is concerned with marking occurrences of a specific object being mentioned. These mentions are then classified into a set of predefined categories. Standard categories include “person”, “location”, “geo-political organization”, “facility”, “organization”, and “time”. The use of NER in text summarization improved the summarization process by increasing the rank of informative sentences. To demonstrate the effectiveness of the proposed approach, we compared its performance on the DUC’04 benchmark document collections with that of a large number of state-of-the-art summarizers. Furthermore, we also performed a qualitative evaluation of the soundness and readability of the generated summaries and a comparison with the results that were produced by the most effective summarizers. A parallel effort has been devoted to integrating semantics-based models and the knowledge acquired from social networks into a document summarization model named as SociONewSum. The effort addresses the sentence-based generic multi-document summarization problem, which can be formulated as follows: given a collection of news articles ranging over the same topic, the goal is to extract a concise yet informative summary, which consists of most salient document sentences. An established ontological model has been used to improve summarization performance by integrating a textual entity recognition and disambiguation step. Furthermore, the analysis of the user-generated content coming from Twitter has been exploited to discover current social trends and improve the appealing of the generated summaries. An experimental evaluation of the SociONewSum performance was conducted on real English-written news article collections and Twitter posts. The achieved results demonstrate the effectiveness of the proposed summarizer, in terms of different ROUGE scores, compared to state-of-the-art open source summarizers as well as to a baseline version of the SociONewSum summarizer that does not perform any UGC analysis. Furthermore, the readability of the generated summaries has also been analyzed

Classificação e agregação automática de notícias desportivas

Mestrado em Engenharia Informática - Área de Especialização em Arquiteturas, Sistemas e RedesEste relatório foi elaborado no âmbito da dissertação para obtenção do Grau de Mestre em Engenharia Informática do Instituto Superior de Engenharia do Porto Foi desenvolvido com vista o auxílio da implementação de um módulo de classificação e agregação (clustering) automática de notícias desportivas. Este módulo será implementado numa aplicação web relacionada com o desporto a ser desenvolvida futuramente. O principal objetivo do trabalho desenvolvido é perceber entre inúmeras possibilidades existentes para classificação e clustering de documentos quais as que melhor se adequam face às exigências necessárias. Aqueles que apresentaram melhores resultados foram os escolhidos para a fase de implementação do módulo de classificação e clustering de notícias. Em primeiro lugar foi realizado um levantamento do estado da arte de forma a se ter conhecimento de todas as possibilidades existentes. Face a essas possibilidades, foram selecionados dois algoritmos para cada um dos temas a abordar. Os algoritmos escolhidos foram aquelas que se verificaram os mais adequados. Para a classificação foram selecionados o Support Vector Machine (SVM) e K-Nearest Neighbors. Para o clustering, algoritmos hierárquicos e o K-means adaptável. Cada uma dessas possibilidades foi devidamente avaliada de forma a perceber quais as melhores soluções face aos problemas propostos. Foi também feita uma breve abordagem à sumarização de documentos, contudo, este é um tema secundário. O principal foco do trabalho desenvolvido é a classificação e clustering de texto. Este trabalho foi feito em cooperação com LIAAD/INESC TEC - Laboratório de Inteligência Artificial e Apoio à Decisão sob a supervisão do Dr. Nuno EscudeiroThis report has been made as part of the Computer Engineering Master’s dissertation from School of Engineering – Polytechnic of Porto. The report has been developed in order to aid the implementation of an automatic process for sports news classification and clustering. That module will be implemented in a web application related with sports. The main goal for this research is to understand among various possibilities which ones fit best given the necessary requirements of the module to be developed. Those who present the best evaluations will be chosen to be implemented in the classification and clustering module. Firstly has been made a survey of the state of the art in order to have knowledge of all possibilities. Given those possibilities, for each topic were selected two algorithms. The chosen algorithms were those that found to be the most suitable. For text categorization were selected the Support Vector Machine (SVM) and the K-Nearest Neighbors (KNN) algorithms. For document clustering, were selected hierarchical algorithms and the adaptable k-means algorithm. Then, each of these possibilities have been properly evaluated in order to understand which are the best solutions. Was also made a brief approach to the documents summarization, however, this is a secondary topic. The main focus of this report is document classification and clustering. This work was made in cooperation with LIAAD/INESC TEC – “Laboratório de Inteligência Artificial e Apoio à Decisão” with supervision of Dr. Nuno Escudeir