PLANTS: A Novel Problem and Dataset for Summarization of Planning-Like (PL) Tasks
<|reference_start|>PLANTS: A Novel Problem and Dataset for Summarization of Planning-Like (PL) Tasks: Text summarization is a well-studied problem that deals with deriving insights from unstructured text consumed by humans, and it has found extensive business applications. However, many real-life tasks involve generating a series of actions to achieve specific goals, such as workflows, recipes, dialogs, and travel plans. We refer to them as planning-like (PL) tasks noting that the main commonality they share is control flow information. which may be partially specified. Their structure presents an opportunity to create more practical summaries to help users make quick decisions. We investigate this observation by introducing a novel plan summarization problem, presenting a dataset, and providing a baseline method for generating PL summaries. Using quantitative metrics and qualitative user studies to establish baselines, we evaluate the plan summaries from our method and large language models. We believe the novel problem and dataset can reinvigorate research in summarization, which some consider as a solved problem.<|reference_end|>
@article{pallagani2024plants:,
    title={PLANTS: A Novel Problem and Dataset for Summarization of Planning-Like
  (PL) Tasks},
    author={Vishal Pallagani, Biplav Srivastava, Nitin Gupta},
    journal={arXiv preprint arXiv:2407.13597},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13597},
    primaryClass={cs.CL cs.AI}
}