[2305.14904] Identifying Informational Sources in News Articles
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Computer Science > Computation and Language

arXiv:2305.14904 (cs)
[Submitted on 24 May 2023]

Title:Identifying Informational Sources in News Articles

Authors:Alexander Spangher, Nanyun Peng, Jonathan May, Emilio Ferrara
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Abstract:News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We show that our dataset can be used to train high-performing models for information detection and source attribution. We further introduce a novel task, source prediction, to study the compositionality of sources in news articles. We show good performance on this task, which we argue is an important proof for narrative science exploring the internal structure of news articles and aiding in planning-based language generation, and an important step towards a source-recommendation system to aid journalists.
Comments: 13 pages
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2305.14904 [cs.CL]
  (or arXiv:2305.14904v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2305.14904

Submission history

From: Alexander Spangher [view email]
[v1] Wed, 24 May 2023 08:56:35 UTC (73 KB)
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