Text summarization as controlled search

Abstract

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.