H um S um: A Personalized Lecture Summarization Tool for Humanities Students Using LLM s (2024)

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.

## introduction and motivation the educational landscape underwent a significant shift with the advent of e-learning platforms and educational applications, particularly during the global pandemic. the increased reliance on remote learning highlighted the necessity of digital tools to support students' educational needs @xcite @xcite . the surge in online learning applications underscores the critical need for personalized tools capable of meeting diverse student requirements. one-size-fits-all approaches often struggle to accommodate the varied learning paces, preferences, and strengths of individual students @xcite ). an essential aspect lies in creating educational applications and tools that go beyond generic content delivery, ensuring tai-lored experiences that resonate with each student's unique learning trajectory. personalization not only enhances academic outcomes but can also serve as a motivational factor, fostering student enthusiasm and persistence in learning endeavors @xcite . the evolution of large language models (llms) represents a recent advancement in artificial intelligence, with the possibility of significantly influencing various facets of education. models such as pre-trained gpt-3/4 @xcite , llama models @xcite , or dialogue-optimized models like instructgpt @xcite , chatgpt (ope-nai, 2022), characterized by their extensive knowledge and language proficiency, have gained attention in many natural language processing (nlp) and natural language generation (nlg) tasks @xcite . llms possess the capability to process vast volumes of text, allowing for nuanced and context-aware understandinga feature fundamental to aligning their output to match the user requests @xcite . the adaptability and versatility inherent in these models enable the development of tailored educational tools and applications, empowering students by providing personalized content that aligns with their learning styles and preferences @xcite @xcite . by harnessing the capabilities of llms, educational platforms can offer interactive and adaptive content, contributing to enhanced engagement, deeper comprehension, and a more impactful learning experience for students across diverse disciplines. we present a personalized summarization tool catered explicitly to the needs of humanities students, harnessing the capabilities of llms to provide tailored lecture summaries. llm-based ap-proaches to automatic summarization have shown promising results in capturing long-range dependencies, handling complex sentence structures, and producing coherent and contextually appropriate summaries @xcite @xcite . our work addresses the following questions: section 3 presents the collection and preprocessing of transcribed university lectures on humanities studies, which were used as material for the summarizations. section 4 presents the design of a survey, centered on scenarios and multiple options, followed by an analysis of survey results highlighting user trends and preferences. these insights have provided the requirements for the development of the summarization tool. section 5 reports on the development of the tool, which was based on ope-nai's gpt-4 model (openai, 2023), illustrating the integration of user preferences derived from the survey into the tool's framework. finally, section 6 presents an in-depth analysis of user engagement and perceptions regarding the tool, evaluating user feedback and satisfaction to assess the effectiveness and quality of personalized summaries. ## lecture transcripts acquisition and preprocessing we created a dataset comprising lecture transcripts suitable for students in the humanities, collecting data from open yale courses (oyc) 1 (kleiner, 2023). this platform hosts numerous courses across various disciplines, including subjects pertinent to humanities students such as english studies, history, psychology, and philosophy. each course includes video content along with supplementary materials like syllabi, suggested readings, and searchable transcripts for every lecture, available in html format. in the preprocessing phase, minimal adjustments were made to the data. verbatim descriptions of non-verbal cues or actions, denoted as '[crosstalk]', '[laughter]', and '[points at student]', among others, which originated from the transcription of video elements, were removed. additionally, indicators of pauses within the transcripts, delimited by timestamps such as [00:08:47], were also eliminated. these modifications were implemented to streamline the content for subsequent processing and analysis. finally, incorporating the lecture transcripts into our summarization tool development involved storing essential details, including departmental information, course titles, professor names, and lecture session numbers, alongside the respective lecture transcripts. ## summarization tool development to craft the summarization tool, we leverage the insights collected from the analysis of scenariodriven survey outcomes. the tool aims at a straightforward design, enabling users to easily engage with the content, and personalizing the summaries to their needs.