Document-Level Abstractive Summarization
This work addresses the problem of expensive computational costs in document-level summarization for researchers and practitioners, though it is incremental as it builds on existing Transformer methods.
The authors tackled document-level abstractive summarization by proposing a retrieval-enhanced Transformer approach to reduce computational costs for long texts, achieving more efficient memory usage and truthfulness but with results below existing baselines.
The task of automatic text summarization produces a concise and fluent text summary while preserving key information and overall meaning. Recent approaches to document-level summarization have seen significant improvements in recent years by using models based on the Transformer architecture. However, the quadratic memory and time complexities with respect to the sequence length make them very expensive to use, especially with long sequences, as required by document-level summarization. Our work addresses the problem of document-level summarization by studying how efficient Transformer techniques can be used to improve the automatic summarization of very long texts. In particular, we will use the arXiv dataset, consisting of several scientific papers and the corresponding abstracts, as baselines for this work. Then, we propose a novel retrieval-enhanced approach based on the architecture which reduces the cost of generating a summary of the entire document by processing smaller chunks. The results were below the baselines but suggest a more efficient memory a consumption and truthfulness.