Papers by Ella Hofmann-Coyle

3 papers
Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders (2022.aacl-short)

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Challenge: Entity-centric summarization is a type of controllable summarizing that aims to produce a summary specific to a given target entity.
Approach: They propose to recast a sentence selection task as a controllable summarization using a dataset supported by EntSUM.
Outcome: The proposed framework outperforms the current state-of-the-art in the sentence selection task and outperformed the competitive entity-centric Lead 3 heuristic by 1.1 F1.
PExA: Parallel Exploration Agent for Complex Text-to-SQL (2026.acl-short)

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Challenge: Recent work in text-to-SQL has explored toolaugmented LLMs, deep planning, and agentic workflows to address complex challenges.
Approach: They validated a framework for text-to-SQL, Spider 2.0, with 70.2% execution accuracy.
Outcome: The proposed framework achieves 70.2% execution accuracy on a state-of-the-art benchmark for text-to-SQL, Spider 2.0.
EntSUMv2: Dataset, Models and Evaluation for More Abstractive Entity-Centric Summarization (2023.emnlp-main)

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Challenge: Entity-centric summarization is a form of controllable summarizing that aims to generate a summary for a specific entity given a document.
Approach: They propose to use a more abstract version of the original entity-centric ENTSUM summarization dataset to generate a shorter annotated summary for downstream users.
Outcome: The proposed method is more abstract and uses supervised fine-tuning and large-scale instruction tuning to provide more specific and useful summaries for downstream users.

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