Using Summarization to Discover Argument Facets in Online Idealogical Dialog

By Amita Misra, Pranav Anand, J. E. Fox Tree, Marilyn A. Walker. 2017

63 citations recorded by the source corpus.

More and more of the information available on the web is dialogic, and a significant portion of it takes place in online forum conversations about current social and political topics. We aim to develop tools to summarize what these conversations are about. What are the CENTRAL PROPOSITIONS associated with different stances on an issue, what are the abstract objects under discussion that are central to a speaker's argument? How can we recognize that two CENTRAL PROPOSITIONS realize the same FACET of the argument? We hypothesize that the CENTRAL PROPOSITIONS are exactly those arguments that people find most salient, and use human summarization as a probe for discovering them. We describe our corpus of human summaries of opinionated dialogs, then show how we can identify similar repeated arguments, and group them into FACETS across many discussions of a topic. We define a new task, ARGUMENT FACET SIMILARITY (AFS), and show that we can predict AFS with a .54 correlation score, versus an ngram system baseline of .39 and a semantic textual similarity system baseline of .45.

Subfields: Artificial Intelligence, Summarization, Dialogue & Conversational AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Iwona Mochol
2. Aleksander Wawer
3. Bogdan Gliwa
4. Maciej Biesek
5. Prakhar Ganesh
6. Saket Dingliwal
7. Yang Liu
8. Xiaojun Wan

In Summarization

1. Iwona Mochol
2. Aleksander Wawer
3. Bogdan Gliwa
4. Maciej Biesek
5. Prakhar Ganesh
6. Saket Dingliwal

In Dialogue & Conversational AI

1. Iwona Mochol
2. Aleksander Wawer
3. Bogdan Gliwa
4. Maciej Biesek

Citations in the corpus

• SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (2019, methodology): “Their model, trained on structured text documents of CNN/Daily Mail dataset, was evaluated on the Argumentative Dialogue Summary Corpus (Misra et al., 2015), which, however, contains only 45 dialogues.”
• Abstractive Summarization of Spoken and Written Conversation (2019, background): “The Argumentative Dialogue Summary Corpus (Misra et al. 2015) consist of 225 summaries, 5 different summaries produced by trained summarizers, of 45 dialogue excerpts on topics like gun control, gay marriage, the death penalty and abortion.”