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20212023
most citedLegal Extractive Summarization of U.S. Court Opinions

6 citations · 7 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CL2021

The Choice of Knowledge Base in Automated Claim Checking

Dominik Stammbach, Boya Zhang, Elliott Ash

Automated claim checking is the task of determining the veracity of a claim given evidence found in a knowledge base of trustworthy facts. While previous work has taken the knowled…

econ.GN2021

RELATIO: Text Semantics Capture Political and Economic Narratives

Elliott Ash, Germain Gauthier, Philine Widmer

Social scientists have become increasingly interested in how narratives -- the stories in fiction, politics, and life -- shape beliefs, behavior, and government policies. This pape…

cs.CL2021★ 1 cited

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…

cs.CL2021

DocSCAN: Unsupervised Text Classification via Learning from Neighbors

Dominik Stammbach, Elliott Ash

We introduce DocSCAN, a completely unsupervised text classification approach using Semantic Clustering by Adopting Nearest-Neighbors (SCAN). For each document, we obtain semantical…

cs.CL2021

Evaluating Document Representations for Content-based Legal Literature Recommendations

Malte Ostendorff, Elliott Ash, Terry Ruas +3

Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflec…