The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Identifying topics by position
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Developing learning strategies for topic-based summarization
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
FastSum: fast and accurate query-based multi-document summarization
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Automatic text summarization of newswire: lessons learned from the document understanding conference
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
The automatic creation of literature abstracts
IBM Journal of Research and Development
Generic multi-document summarization using topic-oriented information
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Comments-oriented document summarization based on multi-aspect co-feedback ranking
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
| Hi-index | 0.00 |
Position information has been proved to be very effective in document summarization, especially in generic summarization. Existing approaches mostly consider the information of sentence positions in a document, based on a sentence position hypothesis that the importance of a sentence decreases with its distance from the beginning of the document. In this paper, we consider another kind of position information, i.e., the word position information, which is based on the ordinal positions of word appearances instead of sentence positions. An extractive summarization model is proposed to provide an evaluation framework for the position information. The resulting systems are evaluated on various data sets to demonstrate the effectiveness of the position information in different summarization tasks. Experimental results show that word position information is more effective and adaptive than sentence position information.