Search Results for author:
Found 10 papers, 8 papers with code
Do Discourse Indicators Reflect the Main Arguments in Scientific Papers?
In scientific papers, arguments are essential for explaining authors’ findings.
Large language models surpass human experts in predicting neuroscience results
LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.
MemSum-DQA: Adapting An Efficient Long Document Extractive Summarizer for Document Question Answering
We introduce MemSum-DQA, an efficient system for document question answering (DQA) that leverages MemSum, a long document extractive summarizer.
SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation
We propose SciLit, a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper, taking into consideration the user-provided context and keywords.
Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information
This implicit nature of conclusion positions makes the automatic segmentation of scientific abstracts into premises and conclusions a challenging task.
Legal Extractive Summarization of U.S. Court Opinions
This paper tackles the task of legal extractive summarization using a dataset of 430K U. S. court opinions with key passages annotated.
Controllable Citation Sentence Generation with Language Models
Citation generation aims to generate a citation sentence that refers to a chosen paper in the context of a manuscript.
Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking
The goal of local citation recommendation is to recommend a missing reference from the local citation context and optionally also from the global context.
Ranked #1 on
Citation Recommendation
on FullTextPeerRead
MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes
We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history.
Ranked #1 on
Extractive Text Summarization
on GovReport
Embedding-based Scientific Literature Discovery in a Text Editor Application
Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited.
