A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Introduction to Information Retrieval
Introduction to Information Retrieval
Combining Multiple Features for Automatic Text Summarization through Machine Learning
PROPOR '08 Proceedings of the 8th international conference on Computational Processing of the Portuguese Language
Journal of Biomedical Informatics
The automatic creation of literature abstracts
IBM Journal of Research and Development
Machine-made index for technical literature: an experiment
IBM Journal of Research and Development
Hi-index | 0.03 |
The volume of information available on the Web is increasing rapidly. The need for systems that can automatically summarise documents is becoming ever more desirable. For this reason, text summarisation has quickly grown into a major research area as illustrated by the DUC and TAC conference series. Summarisation systems for Arabic are however still not as sophisticated and as reliable as those developed for languages like English. In this paper we discuss two summarisation systems for Arabic and report on a large user study performed on these systems. The first system, the Arabic Query-Based Text Summarisation System (AQBTSS), uses standard retrieval methods to map a query against a document collection and to create a summary. The second system, the Arabic Concept-Based Text Summarisation System (ACBTSS), creates a query-independent document summary. Five groups of users from different ages and educational levels participated in evaluating our systems