An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics (2207.00939v1)

Abstract: Long documents such as academic articles and business reports have been the standard format to detail out important issues and complicated subjects that require extra attention. An automatic summarization system that can effectively condense long documents into short and concise texts to encapsulate the most important information would thus be significant in aiding the reader's comprehension. Recently, with the advent of neural architectures, significant research efforts have been made to advance automatic text summarization systems, and numerous studies on the challenges of extending these systems to the long document domain have emerged. In this survey, we provide a comprehensive overview of the research on long document summarization and a systematic evaluation across the three principal components of its research setting: benchmark datasets, summarization models, and evaluation metrics. For each component, we organize the literature within the context of long document summarization and conduct an empirical analysis to broaden the perspective on current research progress. The empirical analysis includes a study on the intrinsic characteristics of benchmark datasets, a multi-dimensional analysis of summarization models, and a review of the summarization evaluation metrics. Based on the overall findings, we conclude by proposing possible directions for future exploration in this rapidly growing field.

An Empirical Survey on Long Document Summarization: Datasets, Models, and Metrics

The task of condensing long-form textual content, such as academic papers and detailed business reports, into concise summaries has gained notable attention with the proliferation of digital content. Automatic text summarization, particularly for long documents, is characterized by unique challenges compared to shorter texts due to the complexity and breadth of the content. This paper presents a comprehensive survey on long document summarization, evaluating the primary components within the research context: datasets, models, and metrics.

The authors introduce a multifaceted evaluation of benchmark datasets relevant to long document summarization. They highlight a fundamental distinction in dataset characteristics between short and long documents. Long document datasets typically require handling significantly larger volumes of tokens and maintaining content coherence and coverage across extended narratives. This sets a higher bar for compression ratios and calls for advanced content selection mechanisms compared to short document datasets. It is particularly noteworthy that while long documents possess richer informational depth, summarization must be concise, capturing only the most salient details without losing coherence.

The survey provides an extensive review of summarization models, categorizing them into extractive, abstractive, and hybrid approaches. Extractive models, which rely on identifying and ranking salient sentences from the source, benefit significantly from graph-based architectures enhanced with contextual embeddings, such as those derived from BERT. The paper also outlines the trajectory of neural models, where attention mechanisms in Recurrent Neural Networks (RNNs) and their evolved forms in Transformers play a pivotal role. Particularly, the transition to Transformer-based models has shown promising results due to their ability to map extensive text dependencies efficiently, albeit with limitations in memory complexity which are currently being addressed by efficient attention mechanisms.

The authors dive into the nuances of Transformer adaptations for long documents, emphasizing mechanisms such as efficient attentions like Longformer and BigBird, which allow models to process longer text inputs effectively. The integration of pre-trained models like BART and PEGASUS, which are fine-tuned on summarization tasks, represents the forefront of current research. These models leverage sequence-to-sequence pre-training tasks which align naturally with summarization objectives.

This survey also critically examines the limitations of existing evaluation metrics like ROUGE, which predominantly focuses on n-gram overlap and may not adequately account for semantic coherence or the factual consistency of generated summaries. Newer metrics, incorporating semantic similarity measures like BERTScore, have begun addressing these gaps. However, the paper advocates for the development and adoption of evaluation metrics that can reliably measure the factual accuracy and coherence in generated summaries across longer texts, which is an area that is insufficiently explored.

Importantly, the paper identifies several future directions to drive advancement in long document summarization. Key areas include the integration of discourse-aware models that can leverage document structure efficiently, the exploration of end-to-end neural architectures that incorporate content selection mechanisms inherently, and the need for more diverse and high-quality benchmarks to ensure models are robustly evaluated across varied domains.

In conclusion, this survey illuminates the complex landscape of long document summarization, underscoring the need for sophisticated models and metrics tailored to long text complexities. The insights provided in this paper form a foundational reference for researchers aiming to push the boundaries of automatic summarization and develop solutions that can cater to the increasing demand for efficient information retrieval from long-form content.