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Structured Abstract Generation for Societal Impact Assessment of Dutch Social Science Research Using Large Language Models
Diaz, S. (2025) Structured Abstract Generation for Societal Impact Assessment of Dutch Social Science Research Using Large Language Models.
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| Abstract: | This thesis presents a semi-automated pipeline designed to generate structured, non-technical abstracts that assess the societal impact of Dutch social science research, addressing limitations in existing AI-driven platforms like that of ScoutinScience, which are less effective for non-technical disciplines. Using the OpenAlex database and the llama-4-scout-17b-16e-instruct large language model (LLM), the pipeline retrieves open-access Dutch social science publications mapping them to Clarivate’s “Social \& Cultural” and “Environmental” impact facets. It then produces five-part structured abstracts, emphasizing societal relevance through targeted prompt engineering and a Chain-of-Thought summarization process. A case study demonstrated high semantic fidelity, effective keyword extraction using KeyLLM, and moderate success in articulating societal impact narratives that target The Netherlands. While results affirm the feasibility of using LLMs to enhance research visibility and engagement, challenges remain in policy framing and facet alignment. Nevertheless, this work offers a replicable approach to bridging the gap between academic research and societal stakeholders, and supports the integration of metrics that evaluate the societal impact, into knowledge transfer platforms. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/107552 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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