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Found 6 papers, 4 papers with code
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization
In this paper, we present a denoising sequence-to-sequence (seq2seq) autoencoder via contrastive learning for abstractive text summarization.
Culture-inspired Multi-modal Color Palette Generation and Colorization: A Chinese Youth Subculture Case
Color is an essential component of graphic design, acting not only as a visual factor but also carrying cultural implications.
A Framework and Dataset for Abstract Art Generation via CalligraphyGAN
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.
A Two-Phase Approach for Abstractive Podcast Summarization
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.
Topic-Guided Abstractive Text Summarization: a Joint Learning Approach
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.
A Baseline Analysis for Podcast Abstractive Summarization
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.
