The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
SUMMAC: a text summarization evaluation
Natural Language Engineering
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Evaluation of Automatic Text Summarization Methods Based on Rhetorical Structure Theory
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 02
Arabic/English multi-document summarization with CLASSY: the past and the future
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
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Automatic text summarization is an essential tool in this era of information overloading. In this paper we present an automatic extractive Arabic text summarization system where the user can cap the size of the final summary. It is a direct system where no machine learning is involved. We use a two pass algorithm where in pass one, we produce a primary summary using Rhetorical Structure Theory (RST); this is followed by the second pass where we assign a score to each of the sentences in the primary summary. These scores will help us in generating the final summary. For the final output, sentences are selected with an objective of maximizing the overall score of the summary whose size should not exceed the user selected limit. We used Rouge to evaluate our system generated summaries of various lengths against those done by a (human) news editorial professional. Experiments on sample texts show our system to outperform some of the existing Arabic summarization systems including those that require machine learning.