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Found 5 papers, 1 papers with code
Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature
While transformer-based encoder-decoder models in a vanilla source document-to-summary setting have been extensively studied for abstractive summarization in different domains, their major limitations continue to be entity hallucination (a phenomenon where generated summaries constitute entities not related to or present in source article(s)) and factual inconsistency.
Relating Input Concepts to Convolutional Neural Network Decisions
Many current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN's decision.
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains.
Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles
Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis.
Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge
Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph.
