A Dynamic Reconstruction Approach to Topic Summarization of User-Generated-Content

Abstract

User generated contents (UGCs) from various social media sites give analysts the opportunity to obtain a comprehensive and dynamic view of any topic from multiple heterogeneous information sources. Summarization provides a promising means of distilling the overview of the targeted topic by aggregating and condensing the related UGCs. However, the mass volume, uneven quality, and dynamics of UGCs, pose new challenges that are not addressed by existing multi-document summarization techniques. In this paper, we introduce a timely task of dynamic structural and textual summarization. We generate topic hierarchy from the UGCs as a high level overview and structural guide for exploring and organizing the content. To capture the evolution of events in the content, we propose a unified dynamic reconstruction approach to detect the update points and generate the time-sequence textual summary. To enhance the expressiveness of the reconstruction space, we further use the topic hierarchy to organize the UGCs and the hierarchical subtopics to augment the sentence representation. Experimental comparison with the state-of-the-art summarization models on a multi-source UGC dataset shows the superiority of our proposed methods. Moreover, we conducted a user study on our usability enhancement measures. It suggests that by disclosing some meta information of the summary generation process in the proposed framework, the time-sequence textual summaries can pair with the structural overview of the topic hierarchy to achieve interpretable and verifiable summarization.