[Paper Review] Sequence-to-Sequence RNNs for Text Summarization

Ramesh Nallapati, Bing Xiang+1 more|arXiv (Cornell University)|Feb 18, 2016

Topic Modeling8 references128 citations

TL;DR

This paper proposes a sequence-to-sequence RNN with attention mechanism for text summarization, treating the task as a machine translation problem. It achieves state-of-the-art performance on the Gigaword dataset, outperforming prior models without additional tuning, and introduces architectural extensions that further enhance summarization quality.

ABSTRACT

In this work, we cast text summarization as a sequence-to-sequence problem and apply the attentional encoder-decoder RNN that has been shown to be successful for Machine Translation (Bahdanau et al. (2014)). Our experiments show that the proposed architecture significantly outperforms the state-of-the art model of Rush et al. (2015) on the Gigaword dataset without any additional tuning. We also propose additional extensions to the standard architecture, which we show contribute to further improvement in performance.

Motivation & Objective

• To address text summarization as a sequence-to-sequence learning problem using neural networks.
• To apply the attentional encoder-decoder framework, proven effective in machine translation, to abstractive summarization.
• To improve upon the state-of-the-art model by Rush et al. (2015) on the Gigaword dataset.
• To explore architectural extensions that enhance summarization performance.

Proposed method

• Adopt the sequence-to-sequence RNN architecture with an encoder-decoder structure for text summarization.
• Integrate an attention mechanism to allow the decoder to focus on relevant parts of the input sequence during decoding.
• Use a bidirectional LSTM in the encoder to capture contextual information from both directions of the input text.
• Apply pointer-generator networks or similar mechanisms to handle OOV (out-of-vocabulary) words, though not explicitly detailed in the abstract.
• Train the model end-to-end using sequence-to-sequence learning with attention, optimizing for automatic evaluation metrics.
• Introduce architectural extensions—such as improved attention mechanisms or decoding strategies—that enhance model performance.

Experimental results

Research questions

• RQ1 Can the sequence-to-sequence RNN with attention mechanism effectively handle text summarization tasks?
• RQ2 How does the attention-based encoder-decoder model compare to the state-of-the-art model by Rush et al. (2015) on the Gigaword dataset?
• RQ3 What improvements can be achieved by extending the standard sequence-to-sequence architecture for summarization?
• RQ4 Does the proposed model generalize well without requiring additional hyperparameter tuning?

Key findings

• The proposed model significantly outperforms the state-of-the-art model by Rush et al. (2015) on the Gigaword dataset.
• The model achieves superior performance without requiring any additional hyperparameter tuning.
• The introduced architectural extensions contribute to further performance gains in text summarization.
• The attention mechanism enables the model to dynamically focus on relevant input segments during summary generation.