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researcher

Elliott Ash

ETH Zurich

9 papers hereh-index 201.2k citations78 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3
  • last author5

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • econ.GN1
affiliations
  • ETH Zurich
HomepageORCID 0000-0002-6817-7529
same name
  • Elliott Ash — 14 papers, h 7
  • Elliott Ash — 4 papers, h 2
  • Elliott Ash — 3 papers, h 2
  • Elliott Ash — 3 papers, h 3
  • Elliott Ash — 2 papers, h 3
  • Elliott Ash — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

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

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

collaborators
Showing 2021 · cs.CLShow all

4 papers · 2 filters

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…

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.