ACL 2024long0 citations

Speech vs. Transcript: Does It Matter for Human Annotators in Speech Summarization?

Roshan Sharma, Suwon Shon, Mark Lindsey, Hira Dhamyal, Bhiksha Raj

Abstract

Reference summaries for abstractive speech summarization require human annotation, which can be performed by listening to an audio recording or by reading textual transcripts of the recording. In this paper, we examine whether summaries based on annotators listening to the recordings differ from those based on annotators reading transcripts. Using existing intrinsic evaluation based on human evaluation, automatic metrics, LLM-based evaluation, and a retrieval-based reference-free method, we find that summaries are indeed different based on the source modality, and that speech-based summaries are more factually consistent and information-selective than transcript-based summaries. Transcript-based summaries are impacted by recognition errors in the source, and expert-written summaries are more informative and reliable. We make all the collected data and analysis code public to facilitate the reproduction of our work and advance research in this area.

BibTeX
@inproceedings{sharma-etal-2024-speech,
    title = "Speech vs. Transcript: Does It Matter for Human Annotators in Speech Summarization?",
    author = "Sharma, Roshan  and
      Shon, Suwon  and
      Lindsey, Mark  and
      Dhamyal, Hira  and
      Raj, Bhiksha",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.790/",
    doi = "10.18653/v1/2024.acl-long.790",
    pages = "14779--14797"
}