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Found 15 papers, 12 papers with code
MSˆ2: Multi-Document Summarization of Medical Studies
In support of this goal, we release MSˆ2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20K summaries derived from the scientific literature.
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions
To evaluate the capabilities of current LLMs on this task, we construct KIWI, a dataset of knowledge-intensive writing instructions in the scientific domain.
CARE: Extracting Experimental Findings From Clinical Literature
Focusing on biomedicine, this work presents CARE -- a new IE dataset for the task of extracting clinical findings.
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
Revising scientific papers based on peer feedback is a challenging task that requires not only deep scientific knowledge and reasoning, but also the ability to recognize the implicit requests in high-level feedback and to choose the best of many possible ways to update the manuscript in response.
S2abEL: A Dataset for Entity Linking from Scientific Tables
Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications.
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Scholarly publications are key to the transfer of knowledge from scholars to others.
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
Motivated by our survey, we present LongEval, a set of guidelines for human evaluation of faithfulness in long-form summaries that addresses the following challenges: (1) How can we achieve high inter-annotator agreement on faithfulness scores?
The Semantic Scholar Open Data Platform
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field.
SciFact-Open: Towards open-domain scientific claim verification
While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in a realistic setting against large corpora of scientific literature.
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts.
Generating Scientific Claims for Zero-Shot Scientific Fact Checking
To address this challenge, we propose scientific claim generation, the task of generating one or more atomic and verifiable claims from scientific sentences, and demonstrate its usefulness in zero-shot fact checking for biomedical claims.
A Search Engine for Discovery of Scientific Challenges and Directions
To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery.
MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting
In our work, we address this research gap by proposing a novel framework for CCA as a document-level context extraction and labeling task.
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups
Experiments are conducted on a newly curated evaluation suite, S2-VLUE, that unifies existing automatically-labeled datasets and includes a new dataset of manual annotations covering diverse papers from 19 scientific disciplines.
MS2: Multi-Document Summarization of Medical Studies
In support of this goal, we release MS^2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20k summaries derived from the scientific literature.
