Automatic Summarization of Online Debates

TL;DR

This paper presents a novel pipeline for summarizing online debates by clustering and labeling key discussion topics, utilizing different clustering methods and visualization to aid users in understanding debate content.

Contribution

It introduces a debate summarization pipeline employing clustering, cluster labeling, and visualization, comparing term-based and ontology-driven clustering approaches.

Findings

Both clustering approaches effectively summarize debate topics.

Ontology-driven clustering enhances label quality.

Visualization aids user understanding of debate structure.

Abstract

Debate summarization is one of the novel and challenging research areas in automatic text summarization which has been largely unexplored. In this paper, we develop a debate summarization pipeline to summarize key topics which are discussed or argued in the two opposing sides of online debates. We view that the generation of debate summaries can be achieved by clustering, cluster labeling, and visualization. In our work, we investigate two different clustering approaches for the generation of the summaries. In the first approach, we generate the summaries by applying purely term-based clustering and cluster labeling. The second approach makes use of X-means for clustering and Mutual Information for labeling the clusters. Both approaches are driven by ontologies. We visualize the results using bar charts. We think that our results are a smooth entry for users aiming to receive the first impression about what is discussed within a debate topic containing waste number of argumentations.