Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
Systems that can automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite background knowledge.…
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning…
Scaling Creative Inspiration with Fine-Grained Functional Facets of Product Ideas
Web-scale repositories of products, patents and scientific papers offer an opportunity for building automated systems that scour millions of existing ideas and assist users in discovering novel…
From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in…
S2AMP: A High-Coverage Dataset of Scholarly Mentorship Inferred from Publications
Mentorship is a critical component of academia, but is not as visible as publications, citations, grants, and awards. Despite the importance of studying the quality and impact of mentorship, there…
Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery
Isolated silos of scientific research and the growing challenge of information overload limit awareness across the literature and hinder innovation. Algorithmic curation and recommendation, which…
Infrastructure for rapid open knowledge network development
The past decade has witnessed a growth in the use of knowledge graph technologies for advanced data search, data integration, and query-answering applications. The leading example of a public,…
CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading
When reading a scholarly paper, scientists oftentimes wish to understand how follow-on work has built on or engages with what they are reading. While a paper itself can only discuss prior work, some…
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Abstractive summarization systems today produce fluent and relevant output, but often “hallucinate” statements not supported by the source text. We analyze the connection between hallucinations and…
LIMEADE: From AI Explanations to Advice Taking
Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow an AI to take advice from humans in response to explanations are similarly…