Deep Recurrent Generative Decoder for Abstractive Text Summarization (1708.00625v1)

Abstract: We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.

• The paper introduces a novel decoder that integrates recurrent latent variables with variational inference to enhance abstractive summarization.
• It employs a sequence-to-sequence architecture with deep generative components to effectively capture semantic and syntactic abstractions.
• Extensive experiments on Gigawords, DUC-2004, and LCSTS datasets demonstrate significant ROUGE score improvements, setting new benchmarks in summarization.

Deep Recurrent Generative Decoder for Abstractive Text Summarization: An Overview

The research paper entitled "Deep Recurrent Generative Decoder for Abstractive Text Summarization" proposes an innovative framework for automatic text summarization. The paper leverages a sequence-to-sequence (seq2seq) encoder-decoder architecture integrated with a Deep Recurrent Generative Decoder (DRGD) to enhance the quality of abstractive summaries. The central innovation of the work resides in its use of recurrent latent random models in collaboration with neural variational inference, marking a notable shift from conventional deterministic and purely discriminative models.

Core Contributions and Methodology

The paper introduces a novel deep recurrent generative decoder that models the latent structure of target summaries, addressing a key limitation in existing seq2seq frameworks. By utilizing Variational Auto-Encoders (VAEs) with recurrent decay, the DRGD effectively manages intractable posterior inference for recurrent latent variables, thus improving the model's representational capacity to handle complex and nuanced text structures.

The decoding process in DRGD considers both generative latent variables and deterministic states, ensuring comprehensive coverage of summary content. This approach allows for the capture of explicit semantic and syntactic constructs as demonstrated in the effectiveness of modeling abstractions that align with human-written summaries.

Numerical Results

The framework was rigorously evaluated on standard summarization datasets including Gigawords, DUC-2004, and LCSTS. Extensive experimentation demonstrated that the DRGD model significantly improves upon state-of-the-art benchmark systems. Specifically, the model presented notable enhancements in ROUGE scores, showcasing its superior capability in retaining critical content while reducing redundancy more effectively than other comparatives.

Implications and Future Directions

The paper highlights several important implications of the proposed design. Practically, the integration of latent variable modeling within neural summarization frameworks sets a new paradigm, potentially inspiring subsequent development trajectories in abstractive summarization technologies. Theoretically, the research advances our understanding of how latent structures in text can be inferred and utilized, advancing future applications in natural language processing tasks beyond summarization.

As future work, the authors suggest the incorporation of complementary techniques such as copy mechanisms and coverage models into the DRGD framework to further enhance the robustness and quality of generated summaries. Such directions could amplify the adaptability of the model across varied text genres and domains.

Overall, the work significantly contributes to the field of automatic text summarization by addressing intrinsic challenges in modeling latent structures and demonstrates potentials for further developments in AI-driven text processing systems. The integration of neural variational inference with recurrent structures suggests a robust blueprint for advancing abstractive summary generation and potentially influencing broader applications in machine learning and artificial intelligence.