SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
The paper introduces SummaRuNNer, a novel Recurrent Neural Network (RNN) based sequence model aimed at enhancing the extractive summarization of documents. The authors, Ramesh Nallapati, Feifei Zhai, and Bowen Zhou, propose an approach leveraging the power of RNNs, specifically a GRU-based architecture, to tackle the problem of extractive summarization with notable performance improvements over prior work.
Key Contributions
SummaRuNNer is conceived as a sequence classifier where the task is framed as a sequential binary classification. Each sentence in a document is examined in the order in which it appears to determine if it should be part of the summary. The methodology involves a bi-directional GRU-RNN, which operates on both word-level and sentence-level representations, making for a robust capture of document context.
The authors highlight three primary contributions:
- Performance & Comparability: SummaRuNNer achieves performance that matches or exceeds state-of-the-art models.
- Interpretability: The simplicity of the model's formulation allows for intuitive visualization of its decision-making process, breaking it down by factors such as information content, salience, and novelty.
- Training Regime: A novel training mechanism allows the model to be trained on human-generated abstractive summaries, eliminating the need for sentence-level extractive labels typically required for such tasks.
Model Architecture
SummaRuNNer employs a GRU-based RNN sequence classifier with two layers:
- Word-Level RNN: The first layer processes word embeddings to compute hidden state representations sequentially within a sentence.
- Sentence-Level RNN: The second layer processes these word-level hidden states to produce sentence representations.
The classification layer then makes a binary decision on whether each sentence belongs to the summary, considering:
- Content: Information richness of the sentence.
- Salience: Sentence relevance to the document as a whole.
- Novelty: Redundancy with the currently formed summary.
- Positional Importance: Both the absolute and relative position of the sentence within the document.
A logistic regression layer aggregates these features to generate the final classification.
Training Strategies
SummaRuNNer supports two training regimes:
- Extractive Training: Utilizes a greedy approach to generate sentence-level labels by optimizing for the Rouge score with respect to reference summaries.
- Abstractive Training: A novel approach where an RNN decoder, attached only during training, uses the summary representation as context. This method uses reference summaries to train the model directly without needing intermediate extractive labels.
Experimental Results
Datasets: The model was evaluated using the CNN/Daily Mail corpus, covering approximately 286,722 training documents. It was also tested on the DUC 2002 dataset for out-of-domain evaluation.
Performance:
- On the Daily Mail corpus, SummaRuNNer outperformed existing models on 75-byte summaries, showing significant performance improvements.
- The model demonstrated comparable results at 275-byte summaries, with extractive training yielding better performance.
- On the combined CNN/Daily Mail corpus using full-length F1 Rouge metrics, SummaRuNNer showed statistically significant improvements over the state-of-the-art.
Out-of-Domain Generalization: SummaRuNNer maintained competitive performance on the DUC 2002 dataset, though it fell short of certain graph-based approaches, indicating some limitations in domain adaptation.
Qualitative Analysis
The authors provide visualizations and qualitative examples illustrating how SummaRuNNer makes summarization decisions. The model's interpretability, a key advantage, allows for examination of individual decision factors, assisting in debugging and understanding model behavior.
Implications and Future Directions
Practically, SummaRuNNer offers a powerful framework for extractive summarization tasks, suitable for applications requiring efficient and interpretable summarization solutions. Theoretically, its modular design and novel training regimes present fertile grounds for further research in combining extractive and abstractive summarization techniques. Future work could explore refining abstractive training to better align with extractive performance and investigate hybrid models that seamlessly integrate both summarization approaches.
In conclusion, SummaRuNNer exemplifies a methodologically sound and practically useful advancement in the domain of extractive summarization. Its contributions toward performance enhancement, interpretability, and innovative training paradigms mark significant steps forward, with promising avenues for continued research and development in AI-driven document summarization technologies.