2 code implementations • 26 Aug 2021 • Chujie Zheng, Kunpeng Zhang, Harry Jiannan Wang, Ling Fan, Zhe Wang
In this paper, we present a denoising sequence-to-sequence (seq2seq) autoencoder via contrastive learning for abstractive text summarization.
1 code implementation • 10 Feb 2021 • Yufan Li, Jinggang Zhuo, Ling Fan, Harry Jiannan Wang
Color is an essential component of graphic design, acting not only as a visual factor but also carrying cultural implications.
no code implementations • 2 Dec 2020 • Jinggang Zhuo, Ling Fan, Harry Jiannan Wang
In this paper, we present a creative framework based on Conditional Generative Adversarial Networks and Contextual Neural Language Model to generate abstract artworks that have intrinsic meaning and aesthetic value, which is different from the existing work, such as image captioning and text-to-image generation, where the texts are the descriptions of the images.
no code implementations • 16 Nov 2020 • Chujie Zheng, Kunpeng Zhang, Harry Jiannan Wang, Ling Fan
Podcast summarization is different from summarization of other data formats, such as news, patents, and scientific papers in that podcasts are often longer, conversational, colloquial, and full of sponsorship and advertising information, which imposes great challenges for existing models.
1 code implementation • 20 Oct 2020 • Chujie Zheng, Kunpeng Zhang, Harry Jiannan Wang, Ling Fan, Zhe Wang
We introduce a new approach for abstractive text summarization, Topic-Guided Abstractive Summarization, which calibrates long-range dependencies from topic-level features with globally salient content.
1 code implementation • 24 Aug 2020 • Chujie Zheng, Harry Jiannan Wang, Kunpeng Zhang, Ling Fan
Podcast summary, an important factor affecting end-users' listening decisions, has often been considered a critical feature in podcast recommendation systems, as well as many downstream applications.