Argument Mining: Extracting Arguments from Online Dialogue

Abstract

Online forums are now one of the primary venues for public dialogue on current social and political issues. The related corpora are often huge, covering any topic imaginable. Our aim is to use these dialogue corpora to automatically discover the semantic aspects of arguments that conversants are making across multiple dialogues on a topic. We frame this goal as consisting of two tasks: argument extraction and argument facet similarity. We focus here on the argument extraction task, and show that we can train regressors to predict the quality of extracted arguments with RRSE values as low as .73 for some topics. A secondary goal is to develop regressors that are topic independent: we report results of cross-domain training and domain-adaptation with RRSE values for several topics as low as .72, when trained on topic independent features.

Fill the Gap! Analyzing Implicit Premises between Claims from Online Debates

“…Boltužić andŠnajder (2015) consider the notion of argument similarity between two claims. Similarly, Swanson et al (2015) and Misra et al (2015) consider argument facet similarity.…”

“…For example, summarizing and analyzing arguments on a controversial topic presupposes that can identify and aggregate identical claims. This task has been addressed in the literature under the name of argument recognition (Boltužić andŠnajder, 2014), reason classification (Hasan and Ng, 2014), argument facet similarity (Swanson et al, 2015;Misra et al, 2015), and argument tagging (Sobhani et al, 2015). The task can be decomposed into two subtasks: (1) identifying the main claims for a topic and (2) matching each claim expressed in text to claims identified as the main claims.…”

Summarizing Multi-Party Argumentative Conversations in Reader Comment on News

“…In recent years various authors have begun work on argument mining in on-line discussion forums (e.g., Cabrio and Vilatta (2012); Boltužić anď Snajder (2014); Swanson et al (2015)) and reader comment on news (e.g., Sobhani et al (2015); Carstens and Toni (2015); Sardianos et al (2015)). While sharing some features, such as allowing multiple participants to exchange views, make claims and supply supporting arguments, these two sources of argumentative discourse also exhibit notable differences.…”

“…Some authors have cited summarization as a motivating end-user task, e.g. Swanson et al (2015) and Misra et al (2015). However, both these works aim at summarising an argument on a single topic like "gun control" across multiple dialogues and do not address the summarization of single, multi-party argumentative conversations that may address multiple issues, such as those found in reader comments.…”

“…Ghosh et al (2014), Habernal et al (2014), Swanson et al (2015)). If such elements and relations could be automatically extracted then they could potentially serve as a basis for generating a summary that better reflects the argumentative content of reader comment.…”