Using sentence-selection heuristics to rank text segments in TXTRACTOR

It is hypothesize that ranking text segments via traditional sentence-selection heuristics produces a balanced summary with more useful information than one produced by using segmentation alone.

Abstract

TXTRACTOR is a tool that uses established sentence-selection heuristics to rank text segments, producing summaries that contain a user-defined number of sentences. The purpose of identifying text segments is to maximize topic diversity, which is an adaptation of the Maximal Marginal Relevance criterion used by Carbonell and Goldstein [5]. Sentence selection heuristics are then used to rank the segments. We hypothesize that ranking text segments via traditional sentence-selection heuristics produces a balanced summary with more useful information than one produced by using segmentation alone. The proposed summary is created in a three-step process, which includes 1) sentence evaluation 2) segment identification and 3) segment ranking. As the required length of the summary changes, low-ranking segments can then be dropped from (or higher ranking segments added to) the summary. We compare the output of TXTRACTOR to the output of a segmentation tool based on the TextTiling algorithm to validate the approach.