6 citations · 7 across the 4 of their papers we have counts for
9 papers
Uncovering and Categorizing Social Biases in Text-to-SQL
Yan Liu, Yan Gao, Zhe Su +3
Content Warning: This work contains examples that potentially implicate stereotypes, associations, and other harms that could be offensive to individuals in certain social groups.}…
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…
Revisiting Automated Topic Model Evaluation with Large Language Models
Dominik Stammbach, Vilém Zouhar, Alexander Hoyle +2
Topic models are used to make sense of large text collections. However, automatically evaluating topic model output and determining the optimal number of topics both have been long…
Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data
Dominik Stammbach, Maria Antoniak, Elliott Ash
This paper shows how to use large-scale pre-trained language models to extract character roles from narrative texts without training data. Queried with a zero-shot question-answeri…
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…
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…
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…
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…
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…