Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
The paper introduces Multi-News, the inaugural large-scale dataset for multi-document summarization (MDS) in the news domain, addressing a crucial gap in the field of NLP. Unlike single-document summarization (SDS), MDS systems have lagged due to limited dataset sizes and less leverage of recent advancements in neural models. The Multi-News dataset consists of 56,216 article-summary pairs, substantially surpassing previous datasets, such as DUC 2004 and TAC 2011, which contained under 100 document clusters.
The dataset was curated from newser.com, featuring professional human-written summaries with sufficient document diversity and source variability, critical for training robust models. The paper's key contribution lies not only in providing this dataset but also in proposing a new abstractive hierarchical model, termed as Hi-MAP (Hierarchical MMR-Attention Pointer-generator), which combines traditional extractive strategies with neural abstractive approaches using Maximal Marginal Relevance (MMR) to effectively handle redundancy and relevancy in the summarization task.
The authors benchmark various methods on the Multi-News dataset, incorporating extractive models like LexRank and TextRank, and notable neural architectures such as the pointer-generator network and CopyTransformer. The results illustrate that the Hi-MAP model delivers competitive performance, especially on metrics such as ROUGE scores, compared to previous state-of-the-art methods.
Key Insights and Numerical Results
- Dataset Characteristics: Multi-News comprises over 56,000 pairs with an average of 2,103 words per document and 263 words per summary. It showcases significant diversity, drawing from more than 1,500 distinct news sources.
- Novelty in Abstraction: The analysis indicates that around 17.76% of unigrams in the dataset's summaries are not present in the source documents, underscoring its potential for modeling both extractive and abstractive techniques effectively.
- Performance Metrics: Hi-MAP demonstrated improved R-2 and R-SU scores against the CopyTransformer, with scores reflecting substantial improvements in handling redundancy.
Implications and Future Directions
The introduction of Multi-News marks a substantial advancement in the research on MDS, paving the way for enhanced deep learning models capable of synthesizing comprehensive summaries from varied document clusters. The dataset's size and applicability encourage the adoption of transformer-based models and novel neural architectures in future investigations.
Practically, this work has notable implications for real-world applications involving media summary generation, aiding stakeholders in digesting voluminous information efficiently. Theoretically, it facilitates the exploration of document interrelations, coherence maintenance, and narrative consistency in AI-generated textual outputs.
As NLP continues to evolve, the development of more sophisticated sequence-to-sequence models and the embedding of inter-document context understanding remain viable paths for further refinement. Future research could also focus on scaling input lengths and streamlining coherence in longer summaries, addressing challenges rooted in summarization fidelity and factual consistency.
In summary, the paper contributes significantly to the MDS domain by providing a comprehensive dataset and a novel model architecture, both of which have the potential to catalyze further advancements in understanding and generating cohesive, contextually rich summaries from multiple document sources.