Papers by Jessica Lam
GreedyCAS: Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information (2023.emnlp-main)
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| Challenge: | Abstracts of scientific papers typically contain premises and conclusions, but in non-structured abstracts the concluding information is not marked. |
| Approach: | They propose to use Normalized Mutual Information (NMI) to optimize the NMI score between two segments by assuming that conclusions are strongly semantically linked with preceding premises. |
| Outcome: | The proposed approach outperforms baseline methods on structured abstracts and on non-structured abstracts. |
Evaluating Unsupervised Argument Aligners via Generation of Conclusions of Structured Scientific Abstracts (2024.eacl-short)
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| Challenge: | Scientific abstracts provide a concise summary of research findings. |
| Approach: | They evaluate unsupervised approaches for extracting scientific arguments as aligned premise-conclusion pairs . they find mutual information outperforms other measures on this task . |
| Outcome: | The proposed methods outperform language models on the task of extracting scientific arguments from abstracts. |