A Joint Model for Structure-based News Genre Classification with Application to Text Summarization

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3332–3342 August 1–6, 2021. ©2021 Association for Computational Linguistics 3332 A Joint Model for Structure-based News Genre Classification with Application to Text Summarization Zeyu Dai, Ruihong Huang Department of Computer Science and Engineering Texas A&M University {jzdaizeyu, huangrh}@tamu.edu Abstract Journalists usually organize and present the contents of a news article following a well- defined structure. In this paper, we propose a novel joint model for structure-based news genre classification that simultaneously iden- tifies one of four commonly used news struc- tures (including Inverted Pyramid and three other structures) for a news article as well as recognizes a sequence of news elements within the article that define the correspond- ing news structure. Experiments show that the joint model consistently outperforms its variants that perform two tasks independently, which supports our motivation that preserving the two-way dependencies and constraints be- tween a type of news structure and its sequence of news elements enables the model to better predict both of them. Although being not per- fect, the system predicted news structure type and news elements have improved the perfor- mance of text summarization when incorpo- rated into a recent neural network system. 1 Introduction Journalists usually organize and report the contents of news following a well-defined structure. For ex- ample, when writing news briefs or breaking news, the Inverted Pyramid structure (Pottker, 2003) is often adopted to present the most newsworthy and key events first and then provide any additional details. However, while being commonly used, In- verted Pyramid is not the only news structure, there exist several other commonly used news structures as well, for example, a structure called Kabob is commonly used to present a narrative hook (Myers and Wukasch, 2003) first and then report the main story, where the narrative hook catches the reader’s attention so that reader is willing to keep reading. Recognizing the overall structure of a news article can benefit many NLP tasks and applications, such as text summarization, text segmentation, discourse analysis, information extraction and text quality as- sessment, and many others. Our recent research (Dai et al., 2018) first defines a small set of news elements, specifically five news elements, and then formally defines four commonly used news structures based on their different ways to select and organize news elements. News ele- ments are defined based on their functions in a news story (introducing the main story or event, catching the reader’s attention or providing details, etc.) as well as their writing styles (narrative or expository, also known as modes of discourse). Specifically, five news elements are defined, including two ledes, Standard Lede and Image Lede, with their functions as either introducing the main story or catching the reader’s attention, as well as three other categories, Synopsis, Narration and a catch-all category Body Section. Each news element is realized as a set of one or more consecutive paragraphs in a news article. Using the well-defined news elements, four news structures, Inverted Pyramid, Kabob, Martini …

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