The headline of this paper names a research area originating from the late 50’s but not loosing its popularity until the present time. Moreover, one of the most relevant today’s problems caused by the rapid growth of the Web, which is called information overloading, has increased the necessity of more sophisticated and powerful summarizers. This paper shortly introduces a taxonomy of summarizat...

Automatic summarization is the process of reducing a text Document with a computer program in order to create a summary that retains the most important points of the original document. As The problem of information overload has grown, and as the quantity of data has increased, so has interest in automatic summarization. It is very difficult for human beings to manually summarize large documents...

Automatic text summarization is one of the research goals of Natural Language Processing which relieves humans from studying each and every line in a text document to understand the underlying concepts in it. Automatic text summarization is aimed to create a brief outline of a given text covering the important points in the text. Automatic text summarization can be generic or query specific. Th...

Text Summarization is compressing the source text into a shorter version preserving its information content and overall meaning. It is very complicated for human beings to manually summarize large documents of text. Text summarization plays an important role in the area of natural language processing and text mining. Many approaches use statistics and machine learning techniques to extract sent...

This paper presents a method for detecting words related to a topic (we call them topic words) over time in the stream of documents. Topic words are widely distributed in the stream of documents, and sometimes they frequently appear in the documents, and sometimes not. We propose a method to reinforce topic words with low frequencies by collecting documents from the corpus, and applied Latent D...

This paper presents a text summarization system for the Spanish language that combines classic techniques in automatic summarization with less frequent ones, like anaphora resolution and cohesive markers detection in order to fight the lack of coherence intrinsic to automatic text excerpts.

This work proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sen...