activity
20182021
most citedNutribullets Hybrid: Multi-document Health Summarization

5 citations · 8 across the 3 of their papers we have counts for

collaborators

10 papers

cs.CL20211 cited

Generating Related Work

Darsh J Shah, Regina Barzilay

Communicating new research ideas involves highlighting similarities and differences with past work. Authors write fluent, often long sections to survey the distinction of a new pap…

cs.CL20215 cited

Nutribullets Hybrid: Multi-document Health Summarization

Darsh J Shah, Lili Yu, Tao Lei +1

We present a method for generating comparative summaries that highlights similarities and contradictions in input documents. The key challenge in creating such summaries is the lac…

cs.CL20212 cited

Nutri-bullets: Summarizing Health Studies by Composing Segments

Darsh J Shah, Lili Yu, Tao Lei +1

We introduce \emph{Nutri-bullets}, a multi-document summarization task for health and nutrition. First, we present two datasets of food and health summaries from multiple scientifi…

cs.CL2019

Capturing Greater Context for Question Generation

Luu Anh Tuan, Darsh J Shah, Regina Barzilay

Automatic question generation can benefit many applications ranging from dialogue systems to reading comprehension. While questions are often asked with respect to long documents,…

cs.CL2019

Automatic Fact-guided Sentence Modification

Darsh J Shah, Tal Schuster, Regina Barzilay

Online encyclopediae like Wikipedia contain large amounts of text that need frequent corrections and updates. The new information may contradict existing content in encyclopediae.…

cs.CL2019

Towards Debiasing Fact Verification Models

Tal Schuster, Darsh J Shah, Yun Jie Serene Yeo +3

Fact verification requires validating a claim in the context of evidence. We show, however, that in the popular FEVER dataset this might not necessarily be the case. Claim-only cla…