Extractive Document Summarization
12 papers with code • 1 benchmarks • 2 datasets
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Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.
Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc.
In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences.
We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization.
An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships.
GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state
In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization.