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cs.CL2024
NewsEdits 2.0: Learning the Intentions Behind Updating News
Alexander Spangher, Kung-Hsiang Huang, Hyundong Cho +1
As events progress, news articles often update with new information: if we are not cautious, we risk propagating outdated facts. In this work, we hypothesize that linguistic featur…
cs.CL2023
AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation
Haoyi Qiu, Kung-Hsiang Huang, Jingnong Qu +1
Ensuring factual consistency is crucial for natural language generation tasks, particularly in abstractive summarization, where preserving the integrity of information is paramount…
cs.CL2023
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma +5
Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries. To address this issue,…