Papers with ECC
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control (2022.findings-naacl)
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| Challenge: | Abstractive summarization systems have been shown to be more prone to unfaithful facts . 30% of summaries generated by pre-trained language models suffer from hallucination . |
| Approach: | They propose a method to remedy entity-level extrinsic hallucinations with Entity Coverage Control . they first compute entity coverage precision and prepend the corresponding control code . a further fine-tuning is performed to unlock zero-shot summarization . |
| Outcome: | The proposed method leads to more faithful and salient abstractive summarization in fine-tuning and zero-shot settings. |
ECC: An Emotion-Cause Conversation Dataset for Empathy Response (2025.emnlp-main)
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| Challenge: | Existing empathy dialogue datasets focus on emotion labels while cause annotations are added post hoc. |
| Approach: | They propose an emotion-cause conversation dataset with 2.4K dialogues that can be scalable . they use a framework that utilizes knowledge and large language models to automatically generate dialogues . |
| Outcome: | The proposed dataset can achieve comparable or even superior performance to existing empathy dialogue datasets. |
ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy improves human-machine dialogue systems by enhancing the user's experience. |
| Approach: | They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. |
| Outcome: | Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation. |