Enhancing Abstractive Summarization with PROM: A Phrase-level Copying Mechanism

Konsep Inti

PROM introduces a new PhRase-level cOpying Mechanism to enhance attention on n-grams, improving factuality and performance in abstractive summarization.

Abstrak

PROM is a novel method that enhances phrase copying in abstractive summarization, showing significant improvements in fine-tuning and zero-shot settings. The method contributes to faithfulness, entity coverage, and human evaluation, demonstrating its effectiveness across diverse datasets.
Based on the remarkable achievements of pre-trained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance. This work proposes PROM, a new PhRase-level cOpying Mechanism that enhances attention on n-grams, which can be applied to zero-shot summarization with pre-training. PROM adds an indicator layer to explicitly pick up tokens in n-gram that can be copied from the source and calculates an auxiliary loss for the copying prediction. Empirical studies show that PROM makes significant improvements in fine-tuning on benchmarks. In the zero-shot setting, PROM is utilized in the self-supervised pre-training on raw corpora and provides new general baselines on a wide range of summarization datasets. Further analysis shows that PROM performs more reasonable copying and contributes to faithfulness.
The copying method represents a compromise of extraction and generation, alleviating the problems of inconsistency. The consistency or faithfulness of abstractive summarization remains to be improved. Intrinsic reasons lie in the inherent imperfection of models such as exposure bias while extrinsic reasons may be because of excessive confidence of the language model leading to unfaithful summaries. The copying method computes a copying distribution on the source sequence and then aggregates the copying distribution and the language model distribution. Thus unfamiliar tokens can be directly copied or ignored.
Summarization also has to face data bottleneck issues where high-quality summaries are usually human-generated but show diversity. Language models require large amounts of data for supervised fine-tuning. Copying methods allow an alternative to picking up tokens from the source sequence coping with expressions which the model is unfamiliar with.

Statistik

Empirical studies show that PROM makes significant improvements in fine-tuning.
Our model surpasses all previous copying methods.
Our model shows advantages on recall but little difference on precision.
Human evaluation results show that our model significantly wins BART in faithfulness.
Zero-shot results indicate that our method can achieve better scores with lead bias.

Kutipan

"The proposed PROM encourages phrase-level copying for enhanced attention on n-grams."
"PROM surpasses previous methods by providing significant improvements in both supervised fine-tuning and zero-shot settings."

Wawasan Utama Disaring Dari

PROM

by Xinbei Ma,Ye... pada arxiv.org 02-29-2024

https://arxiv.org/pdf/2305.06647.pdf

Pertanyaan yang Lebih Dalam

How does PROM compare to other state-of-the-art methods for abstractive summarization

PROM stands out among other state-of-the-art methods for abstractive summarization due to its focus on enhancing phrase-level copying mechanisms. By explicitly modeling the copying probability of each source token at the n-gram level, PROM improves the faithfulness and stability of generated summaries. Compared to previous methods like COPYNET, Pointer-Generator, and SAGCopy, PROM shows significant advantages in fine-tuning benchmarks by encouraging precise and reasonable copying. Additionally, PROM's effectiveness is demonstrated in zero-shot settings through self-supervised pre-training on raw corpora.

What are potential limitations or challenges faced by PROM in real-world applications

While PROM offers several benefits for abstractive summarization tasks, there are potential limitations or challenges that may be faced in real-world applications. One challenge could be related to scalability and efficiency when dealing with very large datasets or complex document structures. The computational resources required for training and inference with PROM may increase significantly as the size of the data grows. Another limitation could be related to handling diverse genres or languages effectively; ensuring that PROM performs consistently well across different types of content may require additional tuning or adaptation.
Additionally, maintaining high levels of faithfulness while generating concise and informative summaries can be a delicate balance that might pose challenges in certain scenarios. Ensuring that copied phrases are relevant and accurate without compromising coherence or fluency is an ongoing challenge in abstractive summarization tasks.

How might advancements in natural language processing impact future developments of phrase-level copying mechanisms like PROM

Advancements in natural language processing (NLP) are likely to have a profound impact on future developments of phrase-level copying mechanisms like PROM. As NLP models become more sophisticated and capable of understanding context better, they can potentially enhance the performance of mechanisms like PROM by providing more accurate predictions for token copying based on contextual information.
Furthermore, improvements in pre-training techniques such as self-supervised learning can lead to better generalization capabilities for models like PROM in zero-shot settings. By leveraging larger pre-trained language models and incorporating domain-specific knowledge during training, phrase-level copying mechanisms can become more robust across various domains and datasets.
Overall, advancements in NLP will continue to drive innovation in abstractive summarization techniques like PROM by enabling them to handle larger volumes of data efficiently while maintaining high levels of accuracy and fidelity in summary generation processes.