HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (1905.06566v1)

Abstract: Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these \emph{inaccurate} labels is challenging. Inspired by the recent work on pre-training transformer sentence encoders \cite{devlin:2018:arxiv}, we propose {\sc Hibert} (as shorthand for {\bf HI}erachical {\bf B}idirectional {\bf E}ncoder {\bf R}epresentations from {\bf T}ransformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained {\sc Hibert} to our summarization model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times dataset. We also achieve the state-of-the-art performance on these two datasets.

An Analysis of "HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization"

The paper presents HIBERT, a novel approach for document summarization leveraging Hierarchical Bidirectional Transformers. The authors focus on addressing the limitations of neural extractive summarization models, which typically rely on hierarchical encoders trained with heuristic, sentence-level labels. These labels often lack accuracy due to their rule-based nature, presenting challenges in training complex hierarchical models effectively. In response, the paper introduces HIBERT (HIerarchical Bidirectional Encoder Representations from Transformers), designed to utilize unlabeled data through a novel pre-training strategy for improved document encoding.

Key Contributions and Methodology

The work introduces HIBERT, a hierarchical transformer model, which is pre-trained using an unsupervised learning approach. This approach is influenced by recent advancements in pre-training strategies for sentence encoders. Instead of word-level prediction tasks used in models like BERT, HIBERT predicts entire masked sentences within documents, utilizing bidirectional context for better text representation. This pre-training mechanism allows HIBERT to capture more contextual information effectively, serving as a powerful initialization for document summarization tasks.

The summarization model employing HIBERT involves a two-stage encoder system. It first encodes sentences within the document using a sentence encoder, and subsequently integrates these sentence representations contextually at the document level using a second encoder. This hierarchical structure is optimized for clarity and coherence in extracted summaries.

Results and Performance

The application of pre-trained HIBERT to the CNN/Dailymail and New York Times datasets demonstrates substantial improvements over models initialized randomly. Specifically, HIBERT shows improvements of 1.25 ROUGE on the CNN/Dailymail dataset and 2.0 ROUGE on the New York Times dataset. Moreover, it achieves state-of-the-art performance on both datasets, outperforming prior neural extractive models and even some baselines based on BERT, a benchmark known for its pre-training success. These results underscore the effectiveness of unsupervised pre-training at the document level for enhancing summarization tasks.

Implications and Future Directions

The implications of HIBERT extend beyond improving summarization performance. By effectively pre-training on document-level tasks, it opens avenues for enhanced understanding and processing of long-form text in other areas such as document QA and sentiment analysis. This research reinforces the importance of robust pre-training for hierarchical models, suggesting that tasks historically limited by word-level context can greatly benefit from sentence-level holistic understanding.

Further theoretical exploration can be directed towards refining the hierarchical transformer architecture itself or exploring alternative objectives that might better align with specific downstream tasks. Additionally, as the domain of large-scale data usage grows, the methodologies for fine-tuning and pre-training on substantially varied datasets could lead to even more adaptable architectures.

In conclusion, this paper contributes significantly to the exploration of large-scale pre-training for hierarchical models in NLP, pointing towards promising directions for those engaged in document-inspired AI tasks.