Abstract
Journalists usually organize and present the contents of a news article following a well-defined structure. In this work, we propose a new task to categorize news articles based on their content presentation structures, which is beneficial for various NLP applications. We first define a small set of news elements considering their functions (e.g., introducing the main story or event, catching the reader’s attention and providing details) in a news story and their writing style (narrative or expository), and then formally define four commonly used news article structures based on their selections and organizations of news elements. We create an annotated dataset for structure-based news genre identification, and finally, we build a predictive model to assess the feasibility of this classification task using structure indicative features.- Anthology ID:
- W18-4308
- Volume:
- Proceedings of the Workshop Events and Stories in the News 2018
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, U.S.A
- Venue:
- EventStory
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 61–67
- Language:
- URL:
- https://aclanthology.org/W18-4308
- DOI:
- Cite (ACL):
- Zeyu Dai, Himanshu Taneja, and Ruihong Huang. 2018. Fine-grained Structure-based News Genre Categorization. In Proceedings of the Workshop Events and Stories in the News 2018, pages 61–67, Santa Fe, New Mexico, U.S.A. Association for Computational Linguistics.
- Cite (Informal):
- Fine-grained Structure-based News Genre Categorization (Dai et al., EventStory 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-4308.pdf