Papers by Eliot Brenner
Long Document Summarization in a Low Resource Setting using Pretrained Language Models (2021.acl-srw)
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Ahsaas Bajaj, Pavitra Dangati, Kalpesh Krishna, Pradhiksha Ashok Kumar, Rheeya Uppaal, Bradford Windsor, Eliot Brenner, Dominic Dotterrer, Rajarshi Das, Andrew McCallum
| Challenge: | Existing abstractive summarization methods only achieve 17.9 ROUGE-L in low-resource settings. |
| Approach: | They propose to use a modern abstractive summarization algorithm to extract salient sentences from long documents to improve their performance. |
| Outcome: | The proposed method beats several competitive salience detection baselines and the identified salient sentences agree with independent human labeling by domain experts. |
Unsupervised Multi-View Post-OCR Error Correction With Language Models (2021.emnlp-main)
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| Challenge: | Prior work used text generation techniques or redundancy in similar passages for OCR error correction, which is not appropriate in cases of low corpus redundancies or weak document contextual information. |
| Approach: | They propose to use a pretrained language model to reconcile different OCR views in unsupervised way so that their combination contains fewer errors than each individual view. |
| Outcome: | The proposed model can reconcile multiple OCR views so that their combined version contains fewer errors than the best OCR view. |