Factual Error Correction in Abstractive Summarization
Abstractive summarization is a task that involves generating a concise summary of a given text, while preserving its meaning and accuracy. One crucial aspect of abstractive summarization is factual error correction, which involves identifying and correcting errors in the source text before generating the summary. This article provides an overview of previous research on factual error correction in abstractive summarization and highlights recent advancements in this area.
Previous Research
Most previous research on factual error correction has focused on text summarization, with a particular emphasis on news articles. These studies have primarily employed methods that substitute inconsistent entities from the source text, such as entity-replacement reranking techniques (Chen et al., 2021), autoregressive models for rewriting and perturbation filtering (Cao et al., 2020; Zhu et al., 2021; Adams et al., 2022), and editing strategies that focus on selective deletion (Wan and Bansal, 2022).
Recent Advances
In contrast to these traditional methods, Fabri et al. (2022) employed sentence compression datasets to train their models. This approach allows the models to learn how to compress sentences while preserving their meaning, which can help identify and correct errors more effectively. More recently, Gao et al. (2023) have expanded the focus of these studies to include dialogue summarization.
Key Takeaways
- Factual error correction is a crucial aspect of abstractive summarization that involves identifying and correcting errors in the source text before generating the summary.
- Previous research on factual error correction has primarily focused on text summarization, with a particular emphasis on news articles.
- Traditional methods for factual error correction include entity-replacement reranking techniques, autoregressive models for rewriting and perturbation filtering, and editing strategies that focus on selective deletion.
- Recent advances in factual error correction have involved employing sentence compression datasets to train models, which allows the models to learn how to compress sentences while preserving their meaning.
- Future research on factual error correction may also involve incorporating external knowledge sources, such as lexical resources or world knowledge bases, to improve the accuracy of error detection and correction.
