@proceedings{2017_Sanchan_Aker_Bontcheva,
author = {Sanchan, Nattapong and Aker, Ahmet and Bontcheva, Kalina},
title = {Automatic Summarization of Online Debates},
booktitle = {Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017},
month = {September},
year = {2017},
address = {Varna, Bulgaria},
publisher = {INCOMA Inc.},
pages = {19--27},
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 secodnd 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.},
url = {https://doi.org/10.26615/978-954-452-038-0_003}
}