# Neural Related Work Summarization with a Joint Context-driven Attention Mechanism

## TL;DR

This paper introduces a neural summarization method for related work sections that uses a joint context-driven attention mechanism to incorporate textual and graphic contexts, improving coherence and relevance.

## Contribution

It presents a novel neural seq2seq model with a joint attention mechanism that considers both text and bibliography graphs for better related work summarization.

## Key findings

- Significant improvement over traditional seq2seq models
- Outperforms five classical summarization baselines
- Effective in maintaining topic coherence

## Abstract

Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.