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Recent advances in deep learning, and especially the invention of encoder decoder architectures, has significantly improved the performance of abstractive summarization systems. Code...

This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by 1, 2 and 2,* 1 KT Corporation, Seongnam 13606, Korea 2 Department of Industrial Engineering, Hanyang University, Seoul 04763, Korea * Author to whom correspondence should be addressed. Academic Editor: Valentino Santucci Appl. Sci. 2022, 12(16), 7968; https://doi.org/10.3390/a (registeringDOI) Received: 15 March 2022 / Revised: 1 August 2022 / Accepted: 3 August 2022 / Published: 9 August 2022 Download PDF Citation Export BibTeX EndNote RIS Cite This Paper In natural language processing (NLP), Transformer is widely used and has reached the state-of-the-art level in numerous NLP tasks such as language modeling, summarization, and classification....

Integrating multimodal knowledge for abstractive summarization task is a work-in-progress research area, with present techniques inheriting fusion-then-generation paradigm. Due to semantic gaps between computer vision and natural language processing, current methods often treat multiple data points as separate objects and rely on attention mechanisms to search for connection in order to fuse together. In addition, missing awareness of cross-modal matching from many frameworks leads to performanc...

We report the results of DialogSum Challenge, the shared task on summarizing real-life scenario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different methods to improve the performance of dialogue summarization. Although there is a great improvement over the baseline models regarding automatic evaluation metrics, such as Rouge scores, we find that there is a salient gap between model generated outputs and human annotated sum...