Abstract
The importance of oral history archives in preserving and understanding past experiences is counterbalanced by the challenges encountered in accessing and searching through them, primarily due to their extensive size and the diverse demographics of the speakers. This paper presents an approach combining ASR technology and Transformer-based neural networks into the Asking questions framework. Its primary function is to generate questions accompanied by concise answers that relate to the topics discussed in each interview segment. Additionally, we introduce a semantic continuity model that filters the generated questions, ensuring that only the most relevant ones are retained. This enables a real-time semantic search through thousands of hours of recordings, with the crucial benefit that the speakers’ original words remain unaltered and still semantically align with the query. While the method is exemplified using a specific publicly available archive, its applicability extends universally to datasets of a similar nature.
This research was supported by the Czech Science Foundation (GA CR), project No. GA22-27800S, and by the grant of the University of West Bohemia, project No. SGS-2022-017.
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Švec, J., Bulín, M., Frémund, A., Polák, F. (2024). Asking Questions Framework for Oral History Archives. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14610. Springer, Cham. https://doi.org/10.1007/978-3-031-56063-7_11
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