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C10-2060.pdf
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🔗 Source: preview.aclanthology.org
📊 Size: 208 KB
👤 Author: Feng Jin ; Minlie Huang ; Xiaoyan Zhu
⬇️ Downloads: 34

Summary

The authors compare different learning-to-rank algorithms (SVR, RankNet, and ListNet) and selection strategies (Integer Linear Programming, Maximum Marginal Relevance, and Diversity Penalty) on two benchmark datasets (TAC 2008 and TAC 2009). The results show that pairwise and listwise learning-to-rank algorithms outperform baselines, and the Integer Linear Programming selection strategy generally outperforms other strategies. The study provides a systematic analysis of ranking and selection strategies for extractive summarization, highlighting the effectiveness of learning-to-rank algorithms and Integer Linear Programming for selecting sentences.

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Description

The authors compare different learning-to-rank algorithms (SVR, RankNet, and ListNet) and selection strategies (Integer Linear Programming, Maximum Marginal...

Technical Information

  • File Format: PDF
  • File Size: 208 KB
  • Pages: 9
  • Language: EN
  • Author: Feng Jin ; Minlie Huang ; Xiaoyan Zhu
  • Total Downloads: 34
  • Last Updated: 3 hours ago

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