Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.

Modelling Knowledge Summarization by Evolving Fuzzy Grammar

Abstract: Summarized text is a simplified and condensed versi on of the original text containing highlighted information to help the audience get the gist in a short period of time. Typically, text summarization produces abstract or a paragraph-like outputs by om itting details and irrelevant information. However, the text summary can also be produced in a visualized f orm, such as a chart, graph or table representing a collection of similar cases. The visualized version generates a statistical-like presentation, which o ften involves numerical and ordinal observation of the g athered knowledge from the text. This requires lexi cal syntactic understanding of the text. Essential to a chieve this goal is topic identification, message analysis/interpretation and knowledge summarization generation. The objective of this study is to mode l knowledge summarization problem using the evolving fuzzy grammar technique and we focus on metadata generation for producing visualized knowledge summarization. The process comprises of: (i) identifying the underlying structure of the texts for knowledge sum marization, (ii) represent the identified knowledge for summarization manipulation and (iii) presentation o f the summarized knowledge. A prototype called FTCat© is developed as a proof of concept and we demonstra te its practicality in summarizing news reports.

Summarization of Text, Automatic

Abstract: With the explosion in the quantity of on-line text and multimedia information in recent years, demand for automatic summarization systems is growing. The goal of automatic summarization is to take a source document or documents, extract information content from it, and present the most important content in a condensed form in a manner sensitive to the needs of the user and task. This article summarizes the principal challenges, approaches, and outstanding problems in summarization. It offers an account of evaluation methods as well as areas for future research.

Document Summarization Using NMF and Pseudo Relevance Feedback Based on K-Means Clustering

Abstract: According to the increment of accessible text data source on the internet, it has increased the necessity of the automatic text document summarization. However, the performance of the automatic methods might be poor because the semantic gap between high level user's summary requirement and low level vector representation of machine exists. In this paper, to overcome that problem, we propose a new document summarization method using a pseudo relevance feedback based on clustering method and NMF (non-negative matrix factorization). Relevance feedback is effective technique to minimize the semantic gap of information processing, but the general relevance feedback needs an intervention of a user. Additionally, the refined query without user interference by pseudo relevance feedback may be biased. The proposed method provides an automatic relevance judgment to reformulate query using the clustering method for minimizing a bias of query expansion. The method also can improve the quality of document summarization since the summarized documents are influenced by the semantic features of documents and the expanded query. The experimental results demonstrate that the proposed method achieves better performance than the other document summarization methods.

A Survey on Existing Extractive Text Summarization Techniques

Abstract: This paper presents a survey on different extractive techniques of text summarization. Text Summarization is a challenging problem these days. The rapid development of emerging technologies poses new challenges to this research field. A summary can give an overview of the original document in a shorter period of time. Readers may decide whether or not to read the complete document after going through the summary. Internet is a great advancement in the field of technology. Nowadays, people can search on Internet for everything. Internet provides large amount of information in the form of web pages. Thus, it is impossible for a person to read web pages completely and make the selection of required web pages. So, the need of producing summaries has become more and more widespread. Summarization offers the possibility of finding main points of text and so the user will spend less time on reading the whole document. Different types of summary might be useful in various applications and summarization can be classified based on these types. Summarization is a very useful task that gives support to many other tasks as well as, it takes advantage of the techniques developed for related Natural Language Processing tasks. Keywords— Text Summarization, Natural Language Processing, Centroid, Cluster