Papers with downstream summarization task
When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization (2023.eacl-main)
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Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen McKeown, Tatsunori Hashimoto
| Challenge: | Existing studies have shown that large language models contain linguistic and societal biases, but it is unclear how these biase amplify to downstream tasks. |
| Approach: | They investigate how name-nationality bias propagates from pre-training to downstream tasks . they show that these biases manifest themselves as hallucinations in summarization . |
| Outcome: | The proposed model can reduce the rate of hallucinations, but does not change the types of biases that do appear. |
Dialogue Summarization with Static-Dynamic Structure Fusion Graph (2023.acl-long)
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| Challenge: | Dialogue summarization is a challenging task since it has dynamic interaction nature and inconsistent information flow among various speakers. |
| Approach: | They propose a Static-Dynamic graph-based Dialogue Summarization model which fuses prior knowledge from human expertise and adaptively learns the graph structure in an end-to-end learning fashion. |
| Outcome: | The proposed model can help people capture the highlights of a semi-structured and multi-participant dialogue without reviewing the complex dialogue context. |