Papers by Dominik Schwabe

2 papers
Indicative Summarization of Long Discussions (2023.emnlp-main)

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Challenge: Using large language models, we generate indicative summaries instead of informative summary for long discussions.
Approach: They propose an unsupervised approach to generating indicative summaries using large language models using large-scale language models.
Outcome: The proposed method clusters argument sentences, generates abstractive summaries, and classifies the generated cluster labels into argumentation frames.
SUMMARY WORKBENCH: Unifying Application and Evaluation of Text Summarization Models (2022.emnlp-demos)

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Challenge: Summary Workbench is a tool for developing and evaluating text summarization models.
Approach: They propose a tool for developing and evaluating text summarization models that integrates with Docker plugins and provides visual analysis of models’ strengths and weaknesses.
Outcome: The proposed model and evaluation measures can be easily integrated as Docker-based plugins and provide insights into the models’ strengths and weaknesses.

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