@inproceedings{juan-etal-2023-generating,
    title = "Generating Multiple Questions from Presentation Transcripts: A Pilot Study on Earnings Conference Calls",
    author = "Juan, Yining  and
      Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    editor = "Keet, C. Maria  and
      Lee, Hung-Yi  and
      Zarrie{\ss}, Sina",
    booktitle = "Proceedings of the 16th International Natural Language Generation Conference",
    month = sep,
    year = "2023",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.inlg-main.35/",
    doi = "10.18653/v1/2023.inlg-main.35",
    pages = "449--454",
    abstract = "In various scenarios, such as conference oral presentations, company managers' talks, and politicians' speeches, individuals often contemplate the potential questions that may arise from their presentations. This common practice prompts the research question addressed in this study: to what extent can models generate multiple questions based on a given presentation transcript? To investigate this, we conduct pilot explorations using earnings conference call transcripts, which serve as regular meetings between professional investors and company managers. We experiment with different task settings and methods and evaluate the results from various perspectives. Our findings highlight that incorporating key points retrieval techniques enhances the accuracy and diversity of the generated questions."
}