Query-Focused Opinion Summarization for User-Generated Content

We present a submodular function-based framework for query-focused opinion summarization. Within our framework, relevance ordering produced by a statistical ranker, and information coverage with respect to topic distribution and diverse viewpoints are both encoded as submodular functions. Dispersion...

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Published in:arXiv.org 2016-06
Main Authors: Wang, Lu, Raghavan, Hema, Cardie, Claire, Castelli, Vittorio
Format: Article
Language:English
Subjects:
Coding
Quality assurance
Redundancy
User generated content
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creator Wang, Lu
Raghavan, Hema
Cardie, Claire
Castelli, Vittorio
description We present a submodular function-based framework for query-focused opinion summarization. Within our framework, relevance ordering produced by a statistical ranker, and information coverage with respect to topic distribution and diverse viewpoints are both encoded as submodular functions. Dispersion functions are utilized to minimize the redundancy. We are the first to evaluate different metrics of text similarity for submodularity-based summarization methods. By experimenting on community QA and blog summarization, we show that our system outperforms state-of-the-art approaches in both automatic evaluation and human evaluation. A human evaluation task is conducted on Amazon Mechanical Turk with scale, and shows that our systems are able to generate summaries of high overall quality and information diversity.
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subjects Coding
Quality assurance
Redundancy
User generated content
title Query-Focused Opinion Summarization for User-Generated Content
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