@inproceedings{kolagar-zarcone-2024-humsum,
    title = "{H}um{S}um: A Personalized Lecture Summarization Tool for Humanities Students Using {LLM}s",
    author = "Kolagar, Zahra  and
      Zarcone, Alessandra",
    editor = "Deshpande, Ameet  and
      Hwang, EunJeong  and
      Murahari, Vishvak  and
      Park, Joon Sung  and
      Yang, Diyi  and
      Sabharwal, Ashish  and
      Narasimhan, Karthik  and
      Kalyan, Ashwin",
    booktitle = "Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.personalize-1.4/",
    pages = "36--70",
    abstract = "Generative AI systems aim to create customizable content for their users, with a subsequent surge in demand for adaptable tools that can create personalized experiences. This paper presents HumSum, a web-based tool tailored for humanities students to effectively summarize their lecture transcripts and to personalize the summaries to their specific needs. We first conducted a survey driven by different potential scenarios to collect user preferences to guide the implementation of this tool. Utilizing Streamlit, we crafted the user interface, while Langchain{'}s Map Reduce function facilitated the summarization process for extensive lectures using OpenAI{'}s GPT-4 model. HumSum is an intuitive tool serving various summarization needs, infusing personalization into the tool{'}s functionality without necessitating the collection of personal user data."
}