Hybrid MemNet for Extractive Summarization

TL;DR

The paper introduces Hybrid MemNet, a data-driven deep learning model that effectively captures local and global information for extractive summarization, outperforming existing methods on multiple datasets.

Contribution

It proposes a novel end-to-end neural network architecture that learns unified representations for extractive summarization, integrating local and global sentence information.

Findings

Significant performance improvements over state-of-the-art baselines.

Effective modeling of local and global sentence features.

Validated on multiple datasets with consistent results.

Abstract

Extractive text summarization has been an extensive research problem in the field of natural language understanding. While the conventional approaches rely mostly on manually compiled features to generate the summary, few attempts have been made in developing data-driven systems for extractive summarization. To this end, we present a fully data-driven end-to-end deep network which we call as Hybrid MemNet for single document summarization task. The network learns the continuous unified representation of a document before generating its summary. It jointly captures local and global sentential information along with the notion of summary worthy sentences. Experimental results on two different corpora confirm that our model shows significant performance gains compared with the state-of-the-art baselines.