Generating Incremental Length Summary Based on Hierarchical Topic Coverage Maximization

Abstract

Document summarization is playing an important role in coping with information overload on the Web. Many summarization models have been proposed recently, but few try to adjust the summary length and sentence order according to application scenarios. With the popularity of handheld devices, presenting key information first in summaries of flexible length is of great convenience in terms of faster reading and decision-making and network consumption reduction. Targeting this problem, we introduce a novel task of generating summaries of incremental length. In particular, we require that the summaries should have the ability to automatically adjust the coverage of general-detailed information when the summary length varies. We propose a novel summarization model that incrementally maximizes topic coverage based on the document’s hierarchical topic model. In addition to the standard Rouge-1 measure, we define a new evaluation metric based on the similarity of the summaries’ topic coverage distribution in order to account for sentence order and summary length. Extensive experiments on Wikipedia pages, DUC 2007, and general noninverted writing style documents from multiple sources show the effectiveness of our proposed approach. Moreover, we carry out a user study on a mobile application scenario to show the usability of the produced summary in terms of improving judgment accuracy and speed, as well as reducing the reading burden and network traffic.