PhAITV: A Phrase Author Interaction Topic Viewpoint Model for the Summarization of Reasons Expressed by Polarized Stances

This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints in text documents. It proposes a pipeline framework that is centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint (PhAITV) model. The evaluation is conducted on all the components of the framework. It is mainly based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The framework shows a significant improvement over several configurations and state-of-the-art methods in contrastive summarization on online debate datasets.

[1]  Mohand Boughanem,et al.  VODUM: A Topic Model Unifying Viewpoint, Topic and Opinion Discovery , 2016, ECIR.

[2]  ˇ FilipBoltu.  Back up your Stance: Recognizing Arguments in Online Discussions , 2014 .

[3]  Vincent Ng,et al.  Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates , 2014, EMNLP.

[4]  Iryna Gurevych,et al.  Argumentation Mining in User-Generated Web Discourse , 2016, CL.

[5]  Osmar R. Zaïane,et al.  Extraction and clustering of arguing expressions in contentious text , 2015, Data Knowl. Eng..

[6]  David Vilares,et al.  Detecting Perspectives in Political Debates , 2017, EMNLP.

[7]  Iryna Gurevych,et al.  Identifying Argumentative Discourse Structures in Persuasive Essays , 2014, EMNLP.

[8]  Amita Misra,et al.  Using Summarization to Discover Argument Facets in Online Idealogical Dialog , 2017, NAACL.

[9]  Arjun Mukherjee,et al.  Extracting Verb Expressions Implying Negative Opinions , 2015, AAAI.

[10]  Mark Stevenson,et al.  Evaluating Topic Coherence Using Distributional Semantics , 2013, IWCS.

[11]  Marilyn A. Walker,et al.  Summarizing Dialogic Arguments from Social Media , 2017, ArXiv.

[12]  Clare R. Voss,et al.  Scalable Topical Phrase Mining from Text Corpora , 2014, Proc. VLDB Endow..

[13]  Michael J. Paul,et al.  Summarizing Contrastive Viewpoints in Opinionated Text , 2010, EMNLP.

[14]  Osmar R. Zaïane,et al.  Unsupervised Model for Topic Viewpoint Discovery in Online Debates Leveraging Author Interactions , 2018, ICWSM.

[15]  Jing Jiang,et al.  A Latent Variable Model for Viewpoint Discovery from Threaded Forum Posts , 2013, NAACL.

[16]  Claire Cardie,et al.  Identifying Appropriate Support for Propositions in Online User Comments , 2014, ArgMining@ACL.

[17]  Timothy Baldwin,et al.  Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality , 2014, EACL.

[18]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[19]  Saif Mohammad,et al.  Stance and Sentiment in Tweets , 2016, ACM Trans. Internet Techn..

[20]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[21]  Gerlof Bouma,et al.  Normalized (pointwise) mutual information in collocation extraction , 2009 .

[22]  Vincent Ng,et al.  Stance Classification of Ideological Debates: Data, Models, Features, and Constraints , 2013, IJCNLP.

[23]  Jan Snajder,et al.  Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity , 2015, ArgMining@HLT-NAACL.

[24]  Dragomir R. Radev,et al.  LexRank: Graph-based Lexical Centrality as Salience in Text Summarization , 2004, J. Artif. Intell. Res..

[25]  Osmar R. Zaïane,et al.  Mining contentious documents , 2015, Knowledge and Information Systems.

[26]  Brian Ecker,et al.  Argument Mining: Extracting Arguments from Online Dialogue , 2015, SIGDIAL Conference.

[27]  Osmar R. Zaïane,et al.  Mining Contentious Documents Using an Unsupervised Topic Model Based Approach , 2014, 2014 IEEE International Conference on Data Mining.