Papers by Mohan Dodda
Human-in-the-loop Abstractive Dialogue Summarization (2023.findings-acl)
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| Challenge: | Abstractive dialogue summarization systems are trained to maximize the likelihood of human-written summaries, but there is still a huge gap in generating high-quality summary as determined by humans. |
| Approach: | They propose to incorporate different levels of human feedback into the training process . they ask humans to highlight salient information to be included in summaries . |
| Outcome: | The proposed model captures human-written summaries and compares them with state-of-the-art models on multiple datasets. |