Ranking explanatory sentences for opinion summarization

Authors:
Hyun Duk Kim;Malu G. Castellanos;Meichun Hsu;ChengXiang Zhai;Umeshwar Dayal;Riddhiman Ghosh
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL, USA;HP Laboratories, Palo Alto, CA, USA;HP Laboratories, Palo Alto, CA, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA;HP Laboratories, Palo Alto, CA, USA;HP Laboratories, Palo Alto, CA, USA
Venue:
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Year:
2013

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Abstract

We introduce a novel sentence ranking problem called explanatory sentence extraction (ESE) which aims to rank sentences in opinionated text based on their usefulness for helping users understand the detailed reasons of sentiments (i.e., "explanatoriness"). We propose and study several general methods for scoring the explanatoriness of a sentence. We create new data sets and propose a new measure for evaluation. Experiment results show that the proposed methods are effective, outperforming a state of the art sentence ranking method for standard text summarization.