From Arguments to Key Points: Towards Automatic Argument Summarization

Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, Noam Slonim

Abstract Paper Share

Sentiment Analysis, Stylistic Analysis, and Argument Mining Long Paper

Session 7A: Jul 7 (08:00-09:00 GMT)
Session 8B: Jul 7 (13:00-14:00 GMT)
Abstract: Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed key points, each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, showing promising performance.
You can open the pre-recorded video in a separate window.
NOTE: The SlidesLive video may display a random order of the authors. The correct author list is shown at the top of this webpage.

Similar Papers

A Two-Step Approach for Implicit Event Argument Detection
Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, Eduard Hovy,
A representative figure from paper main.667
Exploring Content Selection in Summarization of Novel Chapters
Faisal Ladhak, Bryan Li, Yaser Al-Onaizan, Kathy McKeown,
A representative figure from paper main.453
Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization
Gaku Morio, Hiroaki Ozaki, Terufumi Morishita, Yuta Koreeda, Kohsuke Yanai,
A representative figure from paper main.298