Hybridization of Social Spiders and Extractions Techniques for Automatic Text Summaries

1. Introduction And Problematic

Every day, the mass of electronic textual information is increasing dramatically making it more and more difficult access to relevant information without the use of special tools. Additionally, access to the content of the texts by rapid and effective ways has become a necessity. It is becoming increasingly difficult to access relevant information without using specific tools, i.e. access to the content of texts by rapid and effective means has become a necessary task.

Currently, one of the major problems for computer scientists is access to the content of information, access itself or in other words the software and hardware infrastructure are no longer an obstacle, and the major problem is the exponential increase in the amount of textual information electronically. This requires the use of more specific tools i.e. access to the content of texts by rapid and effective means has become a necessary task.

A summary of a text is an effective way to represent the contents of the texts and allow quick access to their semantic content. The purpose of a summarization is to produce an abridged text covering most of the content from the source text. As a matter of fact, a summary of the text has rewritten the text in smaller way. Under constraint kept the semantics of a document that is minimized entropy semantics. The purpose of this operation is to help the reader to identify interesting information for him/her without reading the entire document.

“We cannot imagine our daily life, one day without summaries,” (Minel, J., 2004). Newspaper headlines, the first paragraph of a newspaper article, newsletters, weather, tables of results of sports competitions and library are all summarized. Even in the research, the authors of scientific articles must accompany their scientific articles by a summary written by themselves.

Automatic summaries can be used to reduce the search time to find the relevant documents or to reduce the treatment of long texts by identifying the key information.

To make an automatic summary, the current literature presents three approaches:

• •

  Summarisation by extraction;

• •

  Summarisation by understanding;

• •

  Summarisation by classification.

Our current work uses automatic summarization by extraction as it is a simple method to implement that gives good results; Noting that they are three techniques in this approach: Scoring, similarity and prototype. In the previous work, produce the automatic summary by extraction approach consists to use only one technique at a time (Scoring of phrase, Similarity between phrase or prototype) and respects the order of the phrases in the original document.

The first part contribution of our work is to use two methods of summarization at the same time on the quality of summary. The second part and the most important of this work is the proposition to use a bio-inspired method based on the social spiders to the automatic summary. We aim to evaluate the impact of two part of contribution on the quality of summary.

For that we will proceed first in second section by a state of art to get an idea of anterior work: their advantage and lacks, In section three, then we will explain in details our approach proposes, as any research we will experiment our approach in the section four we will talk the experiment environment, then we will interpret the results to come out with a final conclusion.

2. State Of Art

Automatic summarization appeared earlier as a field of research in computer science from the axis of NLP (automatic language processing), HP Luhn (Luhn, H. P., 1958) proposed in 1958 a first approach to the development of automatic abstracts from extracting phrases.

In the early 1960s, HP Edmundson and other participants in the project TRW (Thompson Ramo Wooldridge Inc.) (Edmundson, H. P., 1963) Proposed a new system of automatic summarization where it combined several criteria to assess the relevance of phrases to extract.

These works were made to identify the fundamental ideas around the automatic summarization, such as problems caused by extraction to build summaries (problems of redundancy, incompleteness, break, etc..), the theoretical inadequacy of the use of statistics, or the difficulties to understand a text (from semantic analysis) to summarize.