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.- Anthology ID:
- W17-7703
- Volume:
- Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Editors:
- Mireille Makary, Michael Oakes
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Inc.
- Note:
- Pages:
- 19–27
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-038-0_003
- DOI:
- 10.26615/978-954-452-038-0_003
- Cite (ACL):
- Nattapong Sanchan, Ahmet Aker, and Kalina Bontcheva. 2017. Automatic Summarization of Online Debates. In Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017, pages 19–27, Varna, Bulgaria. INCOMA Inc..
- Cite (Informal):
- Automatic Summarization of Online Debates (Sanchan et al., RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-038-0_003