@inproceedings{cai-etal-2022-generating,
    title = "Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions",
    author = "Cai, Pengshan  and
      Yu, Mo  and
      Liu, Fei  and
      Yu, Hong",
    editor = "Bosselut, Antoine  and
      Chandu, Khyathi  and
      Dhole, Kaustubh  and
      Gangal, Varun  and
      Gehrmann, Sebastian  and
      Jernite, Yacine  and
      Novikova, Jekaterina  and
      Perez-Beltrachini, Laura",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.gem-1.3/",
    doi = "10.18653/v1/2022.gem-1.3",
    pages = "26--42",
    abstract = "Answering how-to questions remains a major challenge in question answering research. A vast number of narrow, long-tail questions cannot be readily answered using a search engine. Moreover, there is little to no annotated data available to develop such systems. This paper makes a first attempt at generating coherent, long-form answers for how-to questions. We propose new architectures, consisting of passage retrieval, subtopic planning and narrative generation, to consolidate multiple relevant passages into a coherent, explanatory answer. Our subtopic planning module aims to produce a set of relevant, diverse subtopics that serve as the backbone for answer generation to improve topic coherence. We present extensive experiments on a WikiHow dataset repurposed for long-form question answering. Empirical results demonstrate that generating narratives to answer how-to questions is a challenging task. Nevertheless, our architecture incorporated with subtopic planning can produce high-quality, diverse narratives evaluated using automatic metrics and human assessment."
}