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Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Recently graph-based methods have been adopted for Abstractive Text Summarization.
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task.
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension
Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding.
Machine Reading Comprehension
Natural Language Understanding
A Frame-based Sentence Representation for Machine Reading Comprehension
Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC).
