HeterSumGraph: Heterogeneous GNN for Document Summarization
What it is
This repository provides the official PyTorch implementation of the ACL 2020 paper 'Heterogeneous Graph Neural Networks for Extractive Document Summarization'. It uses heterogeneous graph structures to model sentence relationships for extractive summarization on datasets like CNN/DailyMail and Multi-News.
The problem it solves
Standard extractive summarization methods often fail to capture complex, type-specific relationships between sentences (e.g., topic vs. entity mentions) that are better represented in heterogeneous graphs.
What you could use it for
- Academic research on graph neural networks for natural language processing
- Extractive document summarization for news articles
- Reproducing baseline results for summarization benchmarks
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.