2.2k citations · 3.1k across the 54 of their papers we have counts for
8 papers · 2 filters
On Generating Extended Summaries of Long Documents
Sajad Sotudeh, Arman Cohan, Nazli Goharian
Prior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it…
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Daniel Khashabi, Arman Cohan, Siamak Shakeri +22
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich l…
ABNIRML: Analyzing the Behavior of Neural IR Models
Sean MacAvaney, Sergey Feldman, Nazli Goharian +2
Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search. However, it is not yet well-understood why these methods ar…
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
Sean MacAvaney, Arman Cohan, Nazli Goharian
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus. Clinici…
SPECTER: Document-level Representation Learning using Citation-informed Transformers
Arman Cohan, Sergey Feldman, Iz Beltagy +2
Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but t…
TLDR: Extreme Summarization of Scientific Documents
Isabel Cachola, Kyle Lo, Arman Cohan +1
We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge…
Fact or Fiction: Verifying Scientific Claims
David Wadden, Shanchuan Lin, Kyle Lo +4
We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and t…
Longformer: The Long-Document Transformer
Iz Beltagy, Matthew E. Peters, Arman Cohan
Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation…