Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics

Prafulla Kumar Choubey, Ruihong Huang


Abstract
We present an actor-critic framework to induce subtopical structures in a news article for news discourse profiling. The model uses multiple critics that act according to known subtopic structures while the actor aims to outperform them. The content structures constitute sentences that represent latent subtopic boundaries. Then, we introduce a hierarchical neural network that uses the identified subtopic boundary sentences to model multi-level interaction between sentences, subtopics, and the document. Experimental results and analyses on the NewsDiscourse corpus show that the actor model learns to effectively segment a document into subtopics and improves the performance of the hierarchical model on the news discourse profiling task.
Anthology ID:
2021.findings-emnlp.137
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1594–1605
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.137
DOI:
10.18653/v1/2021.findings-emnlp.137
Bibkey:
Cite (ACL):
Prafulla Kumar Choubey and Ruihong Huang. 2021. Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1594–1605, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics (Choubey & Huang, Findings 2021)
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https://aclanthology.org/2021.findings-emnlp.137.pdf
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 https://aclanthology.org/2021.findings-emnlp.137.mp4