Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
Selecting text spans for document summaries: heuristics and metrics
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Learning Algorithms for Keyphrase Extraction
Information Retrieval
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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We present a framework for text summarization based on the generate-and-test model. A large set of summaries is generated for all plausible values of six parameters that control a three-stage process that includes segmentation and keyphrase extraction, and a number of features that characterize the document. Quality is assessed by measuring the summaries against the abstract of the summarized document. The large number of summaries produced for our corpus dictates automated validation and fine-tuning of the summary generator. We use supervised machine learning to detect good and bad parameters. In particular, we identify parameters and ranges of their values within which the summary generator might be used with high reliability on documents for which no author's abstract exists.