A
Arman Cohan
Researcher at Allen Institute for Artificial Intelligence
Publications - 111
Citations - 7466
Arman Cohan is an academic researcher from Allen Institute for Artificial Intelligence. The author has contributed to research in topics: Computer science & Automatic summarization. The author has an hindex of 26, co-authored 87 publications receiving 3827 citations. Previous affiliations of Arman Cohan include Georgetown University & University of Washington.
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Proceedings ArticleDOI
SciBERT: A Pretrained Language Model for Scientific Text
Iz Beltagy,Kyle Lo,Arman Cohan +2 more
TL;DR: SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks and demonstrates statistically significant improvements over BERT.
Posted Content
Longformer: The Long-Document Transformer
TL;DR: Following prior work on long-sequence transformers, the Longformer is evaluated on character-level language modeling and achieves state-of-the-art results on text8 and enwik8 and pretrain Longformer and finetune it on a variety of downstream tasks.
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SciBERT: A Pretrained Language Model for Scientific Text
Iz Beltagy,Kyle Lo,Arman Cohan +2 more
TL;DR: This article proposed SciBERT, a pretrained language model based on BERT to address the lack of high-quality, large-scale labeled scientific data, which leverages unsupervised pretraining on a large multi-domain corpus of scientific publications.
Posted Content
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Arman Cohan,Franck Dernoncourt,Doo Soon Kim,Trung Bui,Seokhwan Kim,Walter Chang,Nazli Goharian +6 more
TL;DR: This work proposes the first model for abstractive summarization of single, longer-form documents (e.g., research papers), consisting of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary.
Proceedings ArticleDOI
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Arman Cohan,Franck Dernoncourt,Doo Soon Kim,Trung Bui,Seokhwan Kim,Walter Chang,Nazli Goharian +6 more
TL;DR: The authors propose a hierarchical encoder that models the discourse structure of a document and an attentive discourse-aware decoder to generate the summary, which significantly outperforms state-of-the-art models.