Query-Focused Opinion Summarization for User-Generated Content
This work addresses the problem of generating high-quality, diverse summaries for users needing insights from community QA and blogs, representing a strong specific gain in this domain.
The paper tackles query-focused opinion summarization from user-generated content by proposing a submodular function-based framework that incorporates relevance ordering and information coverage, and it outperforms state-of-the-art methods in automatic and human evaluations.
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.