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Found 7 papers, 0 papers with code
Learning a Product Relevance Model from Click-Through Data in E-Commerce
We propose a novel way to consider samples of different relevance confidence, and come up with a new training objective to learn a robust relevance model with desirable score distribution.
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations.
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study
In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS.
A Neural Approach to Pun Generation
Since sequence-to-sequence models provide an effective technique for text generation, it is promising to investigate these models on the pun generation task.
Towards a Neural Network Approach to Abstractive Multi-Document Summarization
In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS.
Abstractive Document Summarization with a Graph-Based Attentional Neural Model
Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques.
Ranked #12 on
Text Summarization
on CNN / Daily Mail (Anonymized)
