mlsanityData to 14 Oct 2026

Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection

arXiv 2211.04903 ↗First posted 9 Nov 2022cs.CLcited by 0

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Abstract (arXiv)

Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter. We present a pipelined extractive-abstractive approach where the extractive step filters the content that is passed to the abstractive component. Extremely lengthy input also results in a highly skewed dataset towards negative instances for extractive summarization; we thus adopt a margin ranking loss for extraction to encourage separation between positive and negative examples. Our extraction component operates at the constituent level; our approach to this problem enriches the text with spinal tree information which provides syntactic context (in the form of constituents) to the extraction model. We show an improvement of 3.71 Rouge-1 points over best results reported in prior work on an existing novel chapter dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 9 Nov 2022.

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References · 17

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5 more
  1. 2021Recursively Summarizing Books with Human Feedbackcited by 290
  2. 2021BookSum: A Collection of Datasets for Long-form Narrative Summarizationcited by 184
  3. 2018Fast Abstractive Summarization with Reinforce-Selected Sentence Rewritingcited by 158
  4. 2019Crowdsourcing Lightweight Pyramids for Manual Summary Evaluationcited by 39
  5. 2020Exploring Content Selection in Summarization of Novel Chapterscited by 29

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