Can AI Models Summarize Your Diary Entries? Investigating Utility of Abstractive Summarization for Autobiographical Text
Abstract
Journaling is a widely adopted technique, known to improve mental health and well-being by enabling reflection on past events. Large amounts of text in digital journaling applications could hinder the reflection process due to information overload. Abstractive summarization can solve this problem by generating short summaries to quickly glance at and reminisce. In this paper, we present an investigation of the utility of large language models in the context of autobiographical text summarization. We study two approaches to adapt a self-supervised learning (SSL) model to the domain of autobiographical text. One model employs transfer learning using our new autobiographical text summary dataset to fine-tune the SSL model. The second model leverages existing news datasets for high-quality text summarization mixed with our autobiographical summary dataset. We conducted mixed methods research to analyze the performance of these two models. Through objective evaluation using ROUGE and BART scores, we find that both these approaches perform significantly better than the SSL model fine-tuned with only high-quality news datasets, showing the importance of domain adaptation and autobiographical text summary dataset for this task. Secondly, through a subjective evaluation on a crowd-sourcing platform, we evaluated the summaries generated from these models on various quality criteria such as grammar, non-redundancy, structure, and coherence. We found that on all criteria, these summaries…
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AI-driven models can create helpful and accurate summaries of diary entries.
“In fact, a first empirical demonstration has shown that AI-driven models can create helpful and accurate summaries of diary entries (Siriwardhana et al., 2023).”
From Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model (2024) · cites this paper as Siriwardhana et al., 2023
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“…autobio-graphical and unstructured Reddit-title dataset, i.e., autobio-graphical-only , which is motivated by the transfer learning literature (Cachola et al., 2020; Gururangan et al., 2020), and (2) fine-tune with a mix distribution of structured news data and unstructured Reddit data, i.e.,…”
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Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model
Hutmacher, F., Appel, M., & Schwan, S. (2024) · Psychological Inquiry · doi:10.1080/1047840x.2024.2384125 · Cited by 12 · see citing works
- Authors
- Siriwardhana, S., Gupta, C., Kaluarachchi, T., Dissanayake, V., Ellawela, S., & Nanayakkara, S.
- Affiliations
- University of Auckland (NZ)
- National University of Singapore (SG)
- Corresponding author
- Siriwardhana, Shamane
- ORCID
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2023-11-30
- Volume
- 40
- Issue
- 24
- Pages
- 8655-8673
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4389241259
- Cited by
- 6
- References
- 11
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3
- FWCI
- 0.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Human-Computer Interaction (publishedVersion)
- National University of Singapore (submittedVersion)
- Funders
- Assistive Augmentation research
- Entrepreneurial Universities
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- abstractive summarization, autobiographical writing, self-supervised learning, domain adaptation, transfer learning, summary quality evaluation, ChatGPT comparison, information overload, reflection
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