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Related Experiment Video

Updated: Nov 24, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning.

Travis R Goodwin1, Max E Savery1, Dina Demner-Fushman1

  • 1U.S. National Library of Medicine, National Institutes of Health.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|December 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces conditional summarization, creating specific summaries for tasks like question answering. Multi-task fine-tuning (MTFT) enables zero-shot summarization without task-specific data, improving quality.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Traditional automatic summarization focuses on general-purpose document summaries.
  • Specific applications like question answering and literature discovery require tailored summaries.
  • Existing methods struggle with arbitrary conditional summarization due to data scarcity.

Purpose of the Study:

  • To investigate conditional summarization, where summaries are guided by natural language questions or topics.
  • To enable zero-shot conditional summarization by leveraging multi-task fine-tuning (MTFT).
  • To develop and evaluate novel methods for adaptive task-mixing in MTFT.

Main Methods:

  • Explored multi-task fine-tuning (MTFT) on twenty-one diverse natural language tasks.
  • Developed four new summarization datasets for conditional summarization research.
  • Implemented two adaptive task-mixing strategies for improved MTFT.
  • Evaluated zero-shot performance of T5 and BART models.

Main Results:

  • Multi-task fine-tuning significantly improved zero-shot conditional summarization quality.
  • Adaptive task-mixing strategies enhanced the effectiveness of MTFT.
  • Demonstrated the feasibility of zero-shot summarization for specific user needs.
  • New datasets and methods provide resources for future research.

Conclusions:

  • MTFT is a viable approach for enabling zero-shot conditional summarization.
  • Adaptive task-mixing strategies offer a promising direction for improving MTFT.
  • This research advances the development of more specialized and context-aware summarization systems.