@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T HumSum: A Personalized Lecture Summarization Tool for Humanities Students Using LLMs
%A Kolagar, Zahra
%A Zarcone, Alessandra
%Y Deshpande, Ameet
%Y Hwang, EunJeong
%Y Murahari, Vishvak
%Y Park, Joon Sung
%Y Yang, Diyi
%Y Sabharwal, Ashish
%Y Narasimhan, Karthik
%Y Kalyan, Ashwin
%S Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F kolagar-zarcone-2024-humsum
%X 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.
%U https://aclanthology.org/2024.personalize-1.4
%P 36-70
Markdown (Informal)
[HumSum: A Personalized Lecture Summarization Tool for Humanities Students Using LLMs](https://aclanthology.org/2024.personalize-1.4) (Kolagar & Zarcone, PERSONALIZE-WS 2024)
ACL