Guiding Abstractive Dialogue Summarization with Content Planning (2022.findings-emnlp)
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| Challenge: | Existing methods for abstractive dialogue summarization struggle to maintain factual consistency between dialogue and summary. |
| Approach: | They propose a coarse-to-fine model for generating abstractive dialogue summaries and introduce a fact-aware reinforcement learning objective that improves the fact consistency between the dialogue and the generated summary. |
| Outcome: | The proposed model improves the quality of the generated summary, especially in coherence and consistency. |
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