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)
The role of context in question answering systems
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Identifying topics by position
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
ACM Transactions on Speech and Language Processing (TSLP)
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
Document summarization using conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Evaluation of a sentence ranker for text summarization based on Roget's thesaurus
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Toward a gold standard for extractive text summarization
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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In this paper, we describe a sentence position based summarizer that is built based on a sentence position policy, created from the evaluation testbed of recent summarization tasks at Document Understanding Conferences (DUC). We show that the summarizer thus built is able to outperform most systems participating in task focused summarization evaluations at Text Analysis Conferences (TAC) 2008. Our experiments also show that such a method would perform better at producing short summaries (upto 100 words) than longer summaries. Further, we discuss the baselines traditionally used for summarization evaluation and suggest the revival of an old baseline to suit the current summarization task at TAC: the Update Summarization task.