Implicit sensitive text summarization based on data conveyed by connectives

Henda Chorfi

doi:10.14569/ijacsa.2013.041101

Abstract

So far and trying to reach human capabilities, research in automatic summarization has been based on hypothesis that are both enabling and limiting. Some of these limitations are: how to take into account and reflect (in the generated summary) the implicit information conveyed in the text, the author intention, the reader intention, the context influence, the general world knowledge…. Thus, if we want machines to mimic human abilities, then they will need access to this same large variety of knowledge. The implicit is affecting the orientation and the argumentation of the text and consequently its summary. Most of Text Summarizers (TS) are processing as compressing the initial data and they necessarily suffer from information loss. TS are focusing on features of the text only, not on what the author intended or why the reader is reading the text. In this paper, we address this problem and we present a system focusing on acquiring knowledge that is implicit. We principally spotlight the implicit information conveyed by the argumentative connectives such as : but, even, yet …. and their effect on the summary.

Highlights

INTRODUCTIONText summarization has become widely used on the internet. Users of text summarization are countless
Nowadays, text summarization has become widely used on the internet
Topoi are the guarantors of the passage from the argument to the conclusion

Summary

INTRODUCTION

Text summarization has become widely used on the internet. Users of text summarization are countless. Trying to reach human capabilities, research in automatic summarization has been based on hypothesis that are both enabling and limiting Some of these limitations are: how to take into account and reflect (in the generated summary) the implicit information conveyed in the text, the author intention, the reader intention, the context influence, the general world knowledge. The system we present generate argumentative text based on the implicit stored data conveyed by the “argumentative connectives” such as but, little, a little. When those connectives appear in sentences, they impose constraints on the argumentative movement. We summarize the contributions of this paper and introduce future research directions

Types of summarizers

Summarization techniques

Introduction

SYSTEM ARCHITECTURE

FUTURE WORK

CONCLUSION