title: From Sparse to Dense: GPT-4 Summarization with Chain of Density
    Prompting
summary: Selecting the ``right'' amount of information to include in a summary is a
  difficult task. A good summary should be detailed and entity-centric without
  being overly dense and hard to follow. To better understand this tradeoff, we
  solicit increasingly dense GPT-4 summaries with what we refer to as a ``Chain
  of Density'' (CoD) prompt. Specifically, GPT-4 generates an initial
  entity-sparse summary before iteratively incorporating missing salient entities
  without increasing the length. Summaries generated by CoD are more abstractive,
  exhibit more fusion, and have less of a lead bias than GPT-4 summaries
  generated by a vanilla prompt. We conduct a human preference study on 100 CNN
  DailyMail articles and find that that humans prefer GPT-4 summaries that are
  more dense than those generated by a vanilla prompt and almost as dense as
  human written summaries. Qualitative analysis supports the notion that there
  exists a tradeoff between informativeness and readability. 500 annotated CoD
  summaries, as well as an extra 5,000 unannotated summaries, are freely
  available on HuggingFace
  (https://huggingface.co/datasets/griffin/chain_of_density).
paper_authors._id: 64fe6bc848411fc789d33df8
paper_authors.name: Griffin Adams
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paper_authors.fullname: Griffin Adams
paper_authors.type: user
paper_authors.user: griffin
paper_authors._id: 64fe6bc848411fc789d33df9
paper_authors.name: Alexander Fabbri
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paper_authors.fullname: Alexander Fabbri
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paper_authors.user: alexfabbri
paper_authors._id: 64fe6bc848411fc789d33dfa
paper_authors.name: Faisal Ladhak
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paper_authors.fullname: Faisal Ladhak
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paper_authors.user: fladhak
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paper_authors.name: Eric Lehman
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paper_authors._id: 63be486fd7dea2e13e6133a0
paper_authors.avatarUrl: /avatars/79a7b799044ea48a11899284777eb22e.svg
paper_authors.fullname: Eric Lehman
paper_authors.type: user
paper_authors.user: lehmer16
paper_authors._id: 64fe6bc848411fc789d33dfc
paper_authors.name: Noémie Elhadad
paper_title: From Sparse to Dense: GPT-4 Summarization with Chain of Density
    Prompting
paper_summary: Selecting the ``right'' amount of information to include in a summary is a
  difficult task. A good summary should be detailed and entity-centric without
  being overly dense and hard to follow. To better understand this tradeoff, we
  solicit increasingly dense GPT-4 summaries with what we refer to as a ``Chain
  of Density'' (CoD) prompt. Specifically, GPT-4 generates an initial
  entity-sparse summary before iteratively incorporating missing salient entities
  without increasing the length. Summaries generated by CoD are more abstractive,
  exhibit more fusion, and have less of a lead bias than GPT-4 summaries
  generated by a vanilla prompt. We conduct a human preference study on 100 CNN
  DailyMail articles and find that that humans prefer GPT-4 summaries that are
  more dense than those generated by a vanilla prompt and almost as dense as
  human written summaries. Qualitative analysis supports the notion that there
  exists a tradeoff between informativeness and readability. 500 annotated CoD
  summaries, as well as an extra 5,000 unannotated summaries, are freely
  available on HuggingFace
  (https://huggingface.co/datasets/griffin/chain_of_density).
paper_ai_summary: CoD prompt in GPT-4 improves summary density, informativeness, and human preference over vanilla prompts, with tradeoffs between readability and detail.
paper_ai_keywords: GPT-4 Chain of Density (CoD) entity-sparse salient entities human preference study vanilla prompt informativeness readability HuggingFace