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Context aware hierarchical attention for abstractive dialogue summarization.

Niya Yang1,2, Ye Wang3, Yichen Qi4

  • 1Department of Information Security Technology, Jilin Police College, Changchun, 130123, China.

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Summary

This study introduces a Hierarchical Context-aware Attention (HCAtt) network for abstractive dialogue summarization. HCAtt effectively captures multi-level context in dialogues, outperforming existing methods on benchmark datasets.

Keywords:
Abstractive SummarizationDialogue ModelingHierarchical Context-aware Attention (HCAtt)Multi-level Context

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Abstractive dialogue summarization is crucial for condensing conversations.
  • Social dialogues contain complex phenomena like ellipsis and topic shifts.
  • Existing transformer models struggle to utilize multi-level contextual information effectively.

Purpose of the Study:

  • To propose a novel Hierarchical Context-aware Attention (HCAtt) network.
  • To enhance dialogue summarization by incorporating segment-level and utterance-level context.
  • To improve the modeling of intricate dependencies in dialogue data.

Main Methods:

  • Developed a Hierarchical Context-aware Attention (HCAtt) network.
  • Integrated segment-level and utterance-level contextual information within a transformer framework.
  • Hierarchically integrated context levels during query and key transformations.

Main Results:

  • The HCAtt network demonstrated superior performance on benchmark datasets (SAMSum, DialogSum, AMI).
  • The model effectively captures complex dependencies and contextual relationships in dialogue data.
  • Outperformed existing methods in abstractive dialogue summarization tasks.

Conclusions:

  • The HCAtt network is effective for abstractive dialogue summarization.
  • Incorporating hierarchical context significantly improves dialogue understanding.
  • The proposed method addresses limitations of traditional transformer models in handling dialogue complexity.