Get To The Point: Summarization with Pointer-Generator Networks (1704.04368v2)

Abstract: Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.

Get To The Point: Summarization with Pointer-Generator Networks

The paper "Get To The Point: Summarization with Pointer-Generator Networks" introduces a novel approach to addressing the limitations of traditional sequence-to-sequence (seq2seq) models in abstractive text summarization tasks. Traditional seq2seq models are prone to inaccuracies in reproducing factual information and often generate repetitive text. The proposed pointer-generator network and the incorporation of a coverage mechanism aim to mitigate these issues.

Core Contributions

1. Pointer-Generator Network:
   • The hybrid architecture combines the benefits of both extractive and abstractive summarization techniques. This model can copy words directly from the source text via pointing while also retaining the ability to generate novel words.
   • The pointer-generator network computes a generation probability, which acts as a soft switch between copying from the input sequence and generating from a fixed vocabulary. This allows it to handle out-of-vocabulary (OOV) words effectively while maintaining the fluency and grammaticality of the generated text.
   • Empirical results show the pointer-generator model's superior performance over baseline seq2seq models. For instance, it significantly reduces common errors like replacing uncommon words and introducing nonsensical sentences.
2. Coverage Mechanism:
   • The coverage mechanism addresses the problem of repetition by keeping track of the attention distribution over the source text. The coverage vector, updated at each decoding step, informs the attention mechanism of previously attended words, thus discouraging repetition.
   • Moreover, a coverage loss term is introduced to penalize repeated attention implicitly, further enhancing the model’s ability to generate coherent summaries without redundant information.

Results and Observations

The proposed model is benchmarked against the CNN/Daily Mail dataset, which consists of news articles paired with multi-sentence summaries. Key findings include:

• Performance Metrics:
  • The pointer-generator network, combined with the coverage mechanism, improved ROUGE scores across the board. Specifically, it surpasses the state-of-the-art abstractive summarization techniques by at least 2 ROUGE points in ROUGE-1, ROUGE-2, and ROUGE-L metrics.
  • The METEOR scores also saw significant enhancements, indicating better handling of semantic equivalence due to the hybrid model’s ability to balance between copying and generating.
• Reduction in Repetition:
  • Figure \ref{fig_repetition} in the paper illustrates the marked reduction in repeated $n$-grams in the summaries generated by the coverage model compared to both the baseline and the intermediate pointer-generator model without coverage.
  • This validates the efficacy of the coverage mechanism in eliminating repetitive content.
• Abstractiveness:
  • While the model achieves high fidelity to the source text and ensures grammatical correctness, the generated summaries tend to be less abstractive than human-written ones. The paper acknowledges that though the pointer-generator's summaries include novel phrases, they do not reach the level of abstraction seen in the reference summaries.
  • Future work must explore methods to encourage more abstractiveness without sacrificing the accuracy and coherency provided by the current model.

Theoretical and Practical Implications

The proposed methodology offers several theoretical and practical contributions to the field of NLP:

1. Hybrid Approach:
   • The pointer-generator network represents a compelling hybrid approach that bridges the gap between extractive and abstractive summarization, leveraging the strengths of both paradigms.
2. Extension to Other Tasks:
   • While the paper focuses on summarization, the pointer-generator model and coverage mechanism can potentially be extended to other NLP tasks, such as machine translation and question answering, where factual accuracy and avoidance of redundancy are crucial.
3. Future Directions:
   • There is room for future work in improving the abstractiveness of the generated summaries. Techniques such as reinforcement learning and advanced paraphrasing methods could be integrated to enhance model performance.
   • Moreover, further exploration of different coverage mechanisms and their impact on various types of texts could provide deeper insights and refinements.

Conclusion

The paper successfully demonstrates that augmenting seq2seq models with pointer-generator networks and coverage mechanisms effectively addresses significant challenges in abstractive summarization. While it establishes a new benchmark in terms of ROUGE and METEOR metrics, it also lays the foundation for future advancements aimed at improving the abstractive quality of generated summaries. Thus, its contributions are not only empirical but also set a direction for ongoing research in NLP.