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김탁영(Takyoung Kim),김지나(Jina Kim),강형원(Hyeongwon Kang),김수빈(Subin Kim),강필성(Pilsung Kang) 대한산업공학회 2022 대한산업공학회지 Vol.48 No.1
We propose an integrated text summarization and text-to-speech framework which summarizes Korean documents into a few sentences and reads them in a specific person’s voice. In our framework, a pre-trained text summarization model (KoBART) is fine-tuned with an additional news-oriented text summarization dataset. Then, the fine-tuned model is compressed by knowledge distillation (DistilKoBART) to improve computational efficiency. For text-to-speech, Tacotron 2 and Waveglow models are used. To generate a natural speech sample, we design a task-specific transliteration module that converts numeric or English expressions into Korean. The experimental results show that the proposed framework effectively summarizes long documents and provides a human-like synthesized voice. The proposed framework can provide convenience such as fast information delivery to busy modern people or effectively deliver information to users in special situations such as drivers and people with low vision.