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Modeling coherence by ordering paragraphs using pointer networks.

Divesh Pandey1, C Ravindranath Chowdary1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, 221005, India.

Neural Networks : the Official Journal of the International Neural Network Society
|March 18, 2020
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Summary

This study introduces a novel recurrent neural network (RNN) system for ordering paragraphs in multi-document summarization. The proposed model significantly improves document coherence, outperforming baseline methods with a two-fold increase in ordering accuracy.

Keywords:
Ordering paragraphsParagraph embeddingsPointer networks

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Document coherence is crucial for readability and understanding.
  • Ordering sentences and paragraphs is a key challenge in multi-document summarization.
  • Existing cluster-based ordering methods struggle with inter-cluster coherence.

Purpose of the Study:

  • To develop a deep neural network-based system for ordering paragraphs in multi-document summarization.
  • To enhance the coherence of summarized documents by effectively ordering constituent paragraphs.
  • To address the challenge of ordering clusters of sentences identified by topic.

Main Methods:

  • Utilized a recurrent neural network (RNN) encoder-decoder architecture.
  • Employed Universal Sentence Encoder (USE) for paragraph embedding.
  • Integrated an LSTM encoder and a pointer network for predicting paragraph order.
  • Generated datasets from Wikipedia articles for training and evaluation.

Main Results:

  • The proposed RNN-based system demonstrated superior performance compared to baseline models.
  • Achieved a two-fold increase in Kendall's tau values, indicating improved paragraph ordering.
  • The model successfully generated coherent paragraph orderings across multiple datasets.

Conclusions:

  • The RNN-based encoder-decoder system effectively addresses the paragraph ordering problem in multi-document summarization.
  • Deep learning approaches, specifically RNNs with USE and pointer networks, offer a promising direction for enhancing document coherence.
  • The findings suggest a significant advancement in automated text structuring and summarization quality.