Papers by Chris Newell

2 papers
Recent Trends in Linear Text Segmentation: A Survey (2024.findings-emnlp)

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Challenge: Linear text segmentation is the task of automatically tagging text documents with topic shifts . the task is based on coherence modeling and/or local cues to identify topic boundaries .
Approach: They provide an overview of current advances in linear text segmentation . they highlight limitations of available resources and of the task itself .
Outcome: The proposed task is based on the most recent literature and under-explored research directions.
When Cohesion Lies in the Embedding Space: Embedding-Based Reference-Free Metrics for Topic Segmentation (2024.lrec-main)

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Challenge: Recent advances in topic segmentation have led to a surge in interest in reference-free metrics, designed to score a hypothesised segmentation of a document without the need to refer to any expert annotation.
Approach: They propose a common framework for reference-free topic segmentation metrics and a new method for the embedding space.
Outcome: The proposed framework outperforms existing metrics based on human annotations while allowing for conversational data to outperformed other metrics.

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