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  • RANLP 2023 Saxena

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Exploring Abstractive Text Summarisation for Podcasts: A Comparative Study of BART and T5 Models

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Publication date6/09/2023
Number of pages11
<mark>Original language</mark>English
Event14th Conference on Recent Advances in Natural Language Processing - Varna, Bulgaria
Duration: 4/09/20236/09/2023


Conference14th Conference on Recent Advances in Natural Language Processing
Abbreviated titleRANLP 2023
Internet address


Podcasts have become increasingly popular in recent years, resulting in a massive amount of audio content being produced every day. Efficient summarisation of podcast episodes can enable better content management and discovery for users. In this paper, we explore the use of abstractive text summarisation methods to generate high-quality summaries of podcast episodes. We use pre-trained models, BART and T5, to fine-tune on a dataset of Spotify's 100K podcast. We evaluate our models using automated metrics and human evaluation, and find that the BART model fine-tuned on the podcast dataset achieved a higher ROUGE-1 and ROUGE-L score compared to other models, while the T5 model performed better in terms of semantic meaning. The human evaluation indicates that both models produced high-quality summaries that were well received by participants. Our study demonstrates the effectiveness of abstractive summarisation methods for podcast episodes and offers insights for improving the summarisation of audio content.