Papers by Jacob Parnell
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization (2022.acl-long)
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| Challenge: | Multi-document summarization models are limited by limited references and with maximum-likelihood objectives. |
| Approach: | They propose to fine-tune an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents. |
| Outcome: | The proposed model improves on the Multi-News and WCEP datasets with a low-variance estimator . the proposed model also improves the coverage of the input documents . |
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore (2021.acl-short)
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| Challenge: | Neural machine translation models are biased toward limited translation references . BERTScore is a scoring function based on contextual embeddings that overcomes the limitations of n-gram-based metrics. |
| Approach: | They propose to fine-tune models with a new evaluation metric based on contextual embeddings to overcome the limitations of n-gram-based metrics. |
| Outcome: | The proposed training objective improves translations that are different from the translations but close in the contextual embedding space. |
SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization (2024.naacl-long)
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| Challenge: | Existing approaches to cross-lingual summarization are limited due to limited training data. |
| Approach: | They propose to re-use existing multilingual summarization and translation pipelines to perform cross-lingual summaries in a sequence. |
| Outcome: | The proposed approach outperforms existing methods in many languages with only 10% of the fine-tuning samples. |