Leveraging Contextual Information for Effective Entity Salience Detection

Rajarshi Bhowmik, Marco Ponza, Atharva Tendle, Anant Gupta, Rebecca Jiang, Xingyu Lu, Qian Zhao, Daniel Preotiuc-Pietro


Abstract
In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as salient entities, provide useful cues of the aboutness of a document to a reader. Identifying the salience of entities was found helpful in several downstream applications such as search, ranking, and entity-centric summarization, among others. Prior work on salient entity detection mainly focused on machine learning models that require heavy feature engineering. We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. To this end, we conduct a comprehensive benchmarking of four publicly available datasets using models representative of the medium-sized pre-trained language model family. Additionally, we show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task’s uniqueness and complexity.
Anthology ID:
2024.findings-naacl.28
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
395–408
Language:
URL:
https://aclanthology.org/2024.findings-naacl.28
DOI:
Bibkey:
Cite (ACL):
Rajarshi Bhowmik, Marco Ponza, Atharva Tendle, Anant Gupta, Rebecca Jiang, Xingyu Lu, Qian Zhao, and Daniel Preotiuc-Pietro. 2024. Leveraging Contextual Information for Effective Entity Salience Detection. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 395–408, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Leveraging Contextual Information for Effective Entity Salience Detection (Bhowmik et al., Findings 2024)
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https://aclanthology.org/2024.findings-naacl.28.pdf
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 2024.findings-naacl.28.copyright.pdf