134 citations · 147 across the 9 of their papers we have counts for
7 papers · 1 filter
SpiritRAG: A Q&A System for Religion and Spirituality in the United Nations Archive
Yingqiang Gao, Fabian Winiger, Patrick Montjourides +4
Religion and spirituality (R/S) are complex and highly domain-dependent concepts which have long confounded researchers and policymakers. Due to their context-specificity, R/S are…
MemSum-DQA: Adapting An Efficient Long Document Extractive Summarizer for Document Question Answering
Nianlong Gu, Yingqiang Gao, Richard H. R. Hahnloser
We introduce MemSum-DQA, an efficient system for document question answering (DQA) that leverages MemSum, a long document extractive summarizer. By prefixing each text block in the…
SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation
Nianlong Gu, Richard H. R. Hahnloser
Scientific writing involves retrieving, summarizing, and citing relevant papers, which can be time-consuming processes in large and rapidly evolving fields. By making these process…
Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information
Yingqiang Gao, Jessica Lam, Nianlong Gu +1
The abstracts of scientific papers consist of premises and conclusions. Structured abstracts explicitly highlight the conclusion sentences, whereas non-structured abstracts may hav…
Legal Extractive Summarization of U.S. Court Opinions
Emmanuel Bauer, Dominik Stammbach, Nianlong Gu +1
This paper tackles the task of legal extractive summarization using a dataset of 430K U.S. court opinions with key passages annotated. According to automated summary quality metric…
Controllable Citation Sentence Generation with Language Models
Nianlong Gu, Richard H. R. Hahnloser
Citation generation aims to generate a citation sentence that refers to a chosen paper in the context of a manuscript. However, a rigid citation generation process is at odds with…
MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes
Nianlong Gu, Elliott Ash, Richard H. R. Hahnloser
We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information…