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Applying Recent Research Advances to Google Docs

1. Data

1. Self-supervised pre-training results in an ML model that has general language understanding and generation capabilities, but a subsequent fine-tuning stage is critical for the model to adapt to the application domain. We fine-tuned early versions of our model on a corpus of documents with manually-generated summaries that were consistent with typical use cases.However, early versions of this corpus suffered from inconsistencies and high variation because they included many types of documents, as well as many ways to write a summary — e.g., academic abstracts are typically long and detailed, while executive summaries are brief and punchy. This led to a model that was easily confused because it had been trained on so many different types of documents and summaries that it struggled to learn the relationships between any of them. Fortunately, one of the key findings in the Pegasus work was that an effective pre-training phase required less supervised data in the fine-tuning stage. Some summarization benchmarks required as few as 1,000 fine-tuning examples for Pegasus to match the performance of Transformer baselines that saw 10,000+ supervised examples — suggesting that one could focus on quality rather than quantity. We carefully cleaned and filtered the fine-tuning data to contain training examples that were more consistent and represented a coherent definition of summaries. Despite the fact that we reduced the amount of training data, this led to a higher quality model. The key lesson, consistent with recent work in domains like dataset distillation, was that it was better to have a smaller, high quality dataset, than a larger, high-variance dataset.

2. Serving

Once we trained the high quality model, we turned to the challenge of serving the model in production. While the Transformer version of the encoder-decoder architecture is the dominant approach to train models for sequence-to-sequence tasks like abstractive summarization, it can be inefficient and impractical to serve in real-world applications. The main inefficiency comes from the Transformer decoder where we generate the output summary token by token through autoregressive decoding. The decoding process becomes noticeably slow when summaries get longer since the decoder attends to all previously generated tokens at each step. RNNs are a more efficient architecture for decoding since there is no self-attention with previous tokens as in a Transformer model.We used knowledge distillation, which is the process of transferring knowledge from a large model to a smaller more efficient model, to distill the Pegasus model into a hybrid architecture of a Transformer encoder and an RNN decoder. To improve efficiency we also reduced the number of RNN decoder layers. The resulting model had significant improvements in latency and memory footprint while the quality was still on par with the original model. To further improve the latency and user experience, we serve the summarization model using TPUs, which provide significant speed ups and allow more requests to be handled by a single machine.

Ongoing Challenges and Next Steps

While we are excited by the progress so far, there are a few challenges we are continuing to tackle:

• Document coverage: Developing a set of documents for the fine-tuning stage was difficult due to the tremendous variety that exists among documents, and the same challenge is true at inference time. Some of the documents our users create (e.g., meeting notes, recipes, lesson plans and resumes) are not suitable for summarization or can be difficult to summarize. Currently, our model only suggests a summary for documents where it is most confident, but we hope to continue broadening this set as our model improves.

• Evaluation: Abstractive summaries need to capture the essence of a document while being fluent and grammatically correct. A specific document may have many summaries that can be considered correct, and different readers may prefer different ones. This makes it hard to evaluate summaries with automatic metrics only, user feedback and usage statistics will be critical for us to understand and keep improving quality.

• Long documents: Long documents are some of the toughest documents for the model to summarize because it is harder to capture all the points and abstract them in a single summary, and it can also significantly increase memory usage during training and serving. However, long documents are perhaps most useful for the model to automatically summarize because it can help document writers get a head start on this tedious task. We hope we can apply the latest ML advancements to better address this challenge.

Conclusion

Overall, we are thrilled that we can apply recent progress in NLU and NLG to continue assisting users with reading and writing. We hope the automatic suggestions now offered in Google Workspace make it easier for writers to annotate their documents with summaries, and help readers comprehend and navigate documents more easily.

Acknowledgements

The authors would like to thank the many people across Google that contributed to this work: AJ Motika, Matt Pearson-Beck, Mia Chen, Mahdis Mahdieh, Halit Erdogan, Benjamin Lee, Ali Abdelhadi, Michelle Danoff, Vishnu Sivaji, Sneha Keshav, Aliya Baptista, Karishma Damani, DJ Lick, Yao Zhao, Peter Liu, Aurko Roy, Yonghui Wu, Shubhi Sareen, Andrew Dai, Mekhola Mukherjee, Yinan Wang, Mike Colagrosso, and Behnoosh Hariri. .