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Found 4 papers, 3 papers with code
Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization
Abstract Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency.
Hide-and-Seek: A Template for Explainable AI
Lack of transparency has been the Achilles heal of Neural Networks and their wider adoption in industry.
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization
This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations.
ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System
Neural language processing models, on the other hand, have already found application in recommender systems, mainly as a means of encoding user preference data, with the actual textual description of items serving only as side information.
