Neural Abstractive Text Summarization with Sequence-to-Sequence Models (1812.02303v4)

Abstract: In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries. Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding/generation. There are also other concerns, such as efficiency and parallelism for training a model. In this paper, we provide a comprehensive literature survey on different seq2seq models for abstractive text summarization from the viewpoint of network structures, training strategies, and summary generation algorithms. Several models were first proposed for LLMing and generation tasks, such as machine translation, and later applied to abstractive text summarization. Hence, we also provide a brief review of these models. As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. An extensive set of experiments have been conducted on the widely used CNN/Daily Mail dataset to examine the effectiveness of several different neural network components. Finally, we benchmark two models implemented in NATS on the two recently released datasets, namely, Newsroom and Bytecup.

Overview of Neural Abstractive Text Summarization with Sequence-to-Sequence Models

The paper "Neural Abstractive Text Summarization with Sequence-to-Sequence Models" by Shi et al. presents an extensive survey of the developments in applying sequence-to-sequence (seq2seq) models to the task of neural abstractive text summarization. This survey encapsulates a wide array of methods and innovations explored in recent years, focusing on network structures, training strategies, and summary generation algorithms that enhance and adapt seq2seq models for the complex task of generating human-readable, informative summaries from raw text data.

The core contribution of the paper is its comprehensive review and experimental analysis of different seq2seq architectures, originally designed for tasks like machine translation, that have been adapted and refined for text summarization. The exploration is organized around three primary areas: network structure, parameter inference, and decoding/generation strategies.

Network Structures

The paper categorizes network structures into various aspects like attentive mechanisms and the introduction of innovative architectures such as pointer-generator networks, which have been pivotal in managing challenges like handling out-of-vocabulary (OOV) words and capturing salient information from source texts. Techniques such as hierarchical attention, advanced decoder designs, and mechanisms to avoid repetition are explored. These adaptations aim to improve the model's capability to generate coherent and relevant summaries by efficiently orchestrating the attention of seq2seq models.

Training Strategies

The survey explores the training strategies employed to mitigate issues like exposure bias and the inconsistency between training and evaluation metrics. It discusses the use of curriculum learning and the transition to reinforcement learning (RL) approaches, which allow models to optimize over non-differentiable metrics, aligning training objectives more closely with the evaluation measures used in text summarization tasks. The exploration includes algorithms like REINFORCE, MIXER, and self-critical sequence training that have been adapted to improve seq2seq models' performance in generating summaries.

Summary Generation

Effective summary generation requires sophisticated decoding strategies. The paper examines the use of beam search and its variants to improve the quality and diversity of generated summaries. Techniques to promote diversity within generated sequences, thereby enhancing the novelty and informativeness of the summaries, are highlighted.

Implementation and Experiments

An open-source library, Neural Abstractive Text Summarizer (NATS), is developed by the authors for implementing various seq2seq models. This facilitates extensive experimental evaluation, especially on datasets like CNN/Daily Mail, Newsroom, and Bytecup, which serve as benchmarks for summarization tasks. The experiments demonstrate the effectiveness of different network components, providing insights into the practical considerations involved in designing and training summarization models.

Implications and Future Directions

The survey underscores the shift from traditional extractive summarization methods towards abstractive methods empowered by deep learning. By generating novel, human-like summaries, these advances hold promise for applications across various domains requiring information summarization, from news aggregation to scientific literature review.

Looking ahead, further developments could involve integrating larger pre-trained models like Transformers that have shown success across NLP tasks or refining RL algorithms for better alignment with human evaluation metrics. Additionally, improving the diversity of training datasets and decoding strategies could further enhance model performance and applicability.

In conclusion, this paper not only serves as a reference point for researchers exploring the subtleties of text summarization using deep learning but also lays the groundwork for future exploration in this evolving field.