Papers with English summaries
A Hybrid Approach to Cross-lingual Product Review Summarization (2022.emnlp-industry)
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| Challenge: | Existing methods for summarizing product reviews with thousands of reviews are inefficient and time consuming. |
| Approach: | They propose an unsupervised extractive step and a supervised abstractive step to generate a short summary in any language. |
| Outcome: | The proposed model is as good as human written summaries in coherence, informativeness, non-redundancy, and fluency as human summary summators. |
Bias in News Summarization: Measures, Pitfalls and Corpora (2024.findings-acl)
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| Challenge: | Pretrained large language models can reproduce harmful social biases in constrained settings, such as summarization. |
| Approach: | They propose a method to generate input documents with carefully controlled demographic attributes and then apply it to a controlled setting. |
| Outcome: | The proposed method allows to generate input documents with carefully controlled demographic attributes while working with real-world input documents. |
GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization (2023.findings-acl)
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| Challenge: | Existing datasets are limited to newswire text, which is a fraction of extant genres in general and on the Web. |
| Approach: | They present a small but carefully crafted dataset of English summaries in 12 written and spoken genres for evaluation of abstractive summarization. |
| Outcome: | The proposed dataset of English summaries in 12 written and spoken genres is compared with human outputs and compared to untuned and prompt-based approaches. |
OpenMSD: Towards Multilingual Scientific Documents Similarity Measurement (2024.lrec-main)
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| Challenge: | Existing methods for finding related papers in different languages are not effective for multilingual SDSM. |
| Approach: | They propose to use Open-access Multilingual Scientific Documents to develop multilingual SDSM models that adjust and extend state-of-the-art methods for English SDSM tasks. |
| Outcome: | The proposed model outperforms baseline methods on multilingual SDSM tasks while preserving the performance of the existing methods. |