title: Leveraging Contextual Information for Effective Entity Salience
    Detection
summary: 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.
paper_authors._id: 6507a7124a8839a8bd9686bc
paper_authors.name: Rajarshi Bhowmik
paper_authors._id: 6507a7124a8839a8bd9686bd
paper_authors.name: Marco Ponza
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/6fac81cfc779da1f92fb1924627cc214.svg
paper_authors.fullname: Ponza
paper_authors.type: user
paper_authors.user: mponza
paper_authors._id: 6507a7124a8839a8bd9686be
paper_authors.name: Atharva Tendle
paper_authors.status: admin_assigned
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paper_authors.fullname: Atharva Tendle
paper_authors.type: user
paper_authors.user: atendle
paper_authors._id: 6507a7124a8839a8bd9686bf
paper_authors.name: Anant Gupta
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-18T10:28:14.877Z
paper_authors._id: 63efeeb05c2ceb16fc6f7d0e
paper_authors.avatarUrl: /avatars/592e1a865f24f8910b63a50545effe52.svg
paper_authors.fullname: Anant Gupta
paper_authors.type: user
paper_authors.user: anant718
paper_authors._id: 6507a7124a8839a8bd9686c0
paper_authors.name: Rebecca Jiang
paper_authors._id: 6507a7124a8839a8bd9686c1
paper_authors.name: Xingyu Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-18T10:28:51.101Z
paper_authors._id: 6426bd4ca7723d62b53b9069
paper_authors.avatarUrl: /avatars/e3cd3f3a0beb4ff8f1095ced783ff0e1.svg
paper_authors.fullname: luxingyu
paper_authors.type: user
paper_authors.user: happy905
paper_authors._id: 6507a7124a8839a8bd9686c2
paper_authors.name: Qian Zhao
paper_authors._id: 6507a7124a8839a8bd9686c3
paper_authors.name: Daniel Preotiuc-Pietro
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-09-18T16:10:10.607Z
paper_authors._id: 625050f1d2d191ac4332e9c2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1649430742847-noauth.png
paper_authors.fullname: Daniel Preotiuc-Pietro
paper_authors.type: user
paper_authors.user: danielpreotiuc
paper_title: Leveraging Contextual Information for Effective Entity Salience
    Detection
paper_summary: 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.
paper_ai_keywords: salient entities entity-centric summarization feature engineering fine-tuning medium-sized language models cross-encoder style architecture benchmarking publicly available datasets medium-sized pre-trained language model zero-shot prompting instruction-tuned language models