DataBloom

Posted by Mohammad Saleh, Software Engineer, Google Research, Brain Team, and Yinan Wang, Software Engineer, Google Workspace

Information overload is a significant challenge for many organizations and individuals today. It can be overwhelming to keep up with incoming chat messages and documents that arrive at our inbox everyday. This has been exacerbated by the increase in virtual work and remains a challenge as many teams transition to a hybrid work environment with a mix of those working both virtually and in an office. One solution that can address information overload is summarization — for example, to help users improve their productivity and better manage so much information, we recently introduced auto-generated summaries in Google Docs.

Today, we are excited to introduce conversation summaries in Google Chat for messages in Spaces. When these summaries are available, a card with automatically generated summaries is shown as users enter Spaces with unread messages. The card includes a list of summaries for the different topics discussed in Spaces. This feature is enabled by our state-of-the-art abstractive summarization model, Pegasus, which generates useful and concise summaries for chat conversations, and is currently available to selected premium Google Workspace business customers.

Conversation summaries provide a helpful digest of conversations in Spaces, allowing users to quickly catch-up on unread messages and navigate to the most relevant threads.

Conversation Summarization Modeling

The goal of text summarization is to provide helpful and concise summaries for different types of text, such as documents, articles, or spoken conversations. A good summary covers the key points succinctly, and is fluent and grammatically correct. One approach to summarization is to extract key parts from the text and concatenate them together into a summary (i.e., extractive summarization). Another approach is to use natural language generation (NLG) techniques to summarize using novel words and phrases not necessarily present in the original text. This is referred to as abstractive summarization and is considered closer to how a person would generally summarize text. A main challenge with abstractive summarization, however, is that it sometimes struggles to generate accurate and grammatically correct summaries, especially in real world applications.

ForumSum Dataset

The majority of abstractive summarization datasets and research focuses on single-speaker text documents, like news and scientific articles, mainly due to the abundance of human-written summaries for such documents. On the other hand, datasets of human-written summaries for other types of text, like chat or multi-speaker conversations, are very limited.

To address this we created ForumSum, a diverse and high-quality conversation summarization dataset with human-written summaries. The conversations in the dataset are collected from a wide variety of public internet forums, and are cleaned up and filtered to ensure high quality and safe content (more details in the paper).

An example from the ForumSum dataset.

Each utterance in the conversation starts on a new line, contains an author name and a message text that is separated with a colon. Human annotators are then given detailed instructions to write a 1-3 sentence summary of the conversation. These instructions went through multiple iterations to ensure annotators wrote high quality summaries. We have collected summaries for over six thousand conversations, with an average of more than 6 speakers and 10 utterances per conversation. ForumSum provides quality training data for the conversation summarization problem: it has a variety of topics, number of speakers, and number of utterances commonly encountered in a chat application.

Conversation Summarization Model Design

As we have written previously, the Transformer is a popular model architecture for sequence-to-sequence tasks, like abstractive summarization, where the inputs are the document words and the outputs are the summary words. Pegasus combined transformers with self-supervised pre-training customized for abstractive summarization, making it a great model choice for conversation summarization. First, we fine-tune Pegasus on the ForumSum dataset where the input is the conversation words and the output is the summary words. Second, we use knowledge distillation to distill the Pegasus model into a hybrid architecture of a transformer encoder and a recurrent neural network (RNN) decoder. The resulting model has lower latency and memory footprint while maintaining similar quality as the Pegasus model.

Quality and User Experience

A good summary captures the essence of the conversation while being fluent and grammatically correct. Based on human evaluation and user feedback, we learned that the summarization model generates useful and accurate summaries most of the time. But occasionally the model generates low quality summaries. After looking into issues reported by users, we found that there are two main types of low quality summaries. The first one is misattribution, when the model confuses which person or entity said or performed a certain action. The second one is misrepresentation, when the model’s generated summary misrepresents or contradicts the chat conversation.

To address low quality summaries and improve the user experience, we have made progress in several areas:

1. Improving ForumSum: While ForumSum provides a good representation of chat conversations, we noticed certain patterns and language styles in Google Chat conversations that differ from ForumSum, e.g., how users mention other users and the use of abbreviations and special symbols. After exploring examples reported by users, we concluded that these out-of-distribution language patterns contributed to low quality summaries. To address this, we first performed data formatting and clean-ups to reduce mismatches between chat and ForumSum conversations whenever possible. Second, we added more training data to ForumSum to better represent these style mismatches. Collectively, these changes resulted in reduction of low quality summaries.
2. Controlled triggering: To make sure summaries bring the most value to our users, we first need to make sure that the chat conversation is worthy of summarization. For example, we found that there is less value in generating a summary when the user is actively engaged in a conversation and does not have many unread messages, or when the conversation is too short.
3. Detecting low quality summaries: While the two methods above limited low quality and low value summaries, we still developed methods to detect and abstain from showing such summaries to the user when they are generated. These are a set of heuristics and models to measure the overall quality of summaries and whether they suffer from misattribution or misrepresentation issues.

Finally, while the hybrid model provided significant performance improvements, the latency to generate summaries was still noticeable to users when they opened Spaces with unread messages. To address this issue, we instead generate and update summaries whenever there is a new message sent, edited or deleted. Then summaries are cached ephemerally to ensure they surface smoothly when users open Spaces with unread messages.

Conclusion and Future Work

We are excited to apply state-of-the-art abstractive summarization models to help our Workspace users improve their productivity in Spaces. While this is great progress, we believe there are many opportunities to further improve the experience and the overall quality of summaries. Future directions we are exploring include better modeling and summarizing entangled conversations that include multiple topics, and developing metrics that better measure the factual consistency between chat conversations and summaries.

Acknowledgements

The authors would like to thank the many people across Google that contributed to this work: Ahmed Chowdhury, Alejandro Elizondo, Anmol Tukrel, Benjamin Lee, Chao Wang, Chris Carroll, Don Kim, Jackie Tsay, Jennifer Chou, Jesse Sliter, John Sipple, Kate Montgomery, Maalika Manoharan, Mahdis Mahdieh, Mia Chen, Misha Khalman, Peter Liu, Robert Diersing, Sarah Read, Winnie Yeung, Yao Zhao, and Yonghui Wu.

Posted by Mahima Pushkarna, Senior Interaction Designer, and Andrew Zaldivar, Senior Developer Relations Engineer, Google Research

As machine learning (ML) research moves toward large-scale models capable of numerous downstream tasks, a shared understanding of a dataset’s origin, development, intent, and evolution becomes increasingly important for the responsible and informed development of ML models. However, knowledge about datasets, including use and implementations, is often distributed across teams, individuals, and even time. Earlier this year at the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), we published Data Cards, a dataset documentation framework aimed at increasing transparency across dataset lifecycles. Data Cards are transparency artifacts that provide structured summaries of ML datasets with explanations of processes and rationale that shape the data and describe how the data may be used to train or evaluate models. At minimum, Data Cards include the following: (1) upstream sources, (2) data collection and annotation methods, (3) training and evaluation methods, (4) intended use, and (5) decisions affecting model performance.

In practice, two critical factors determine the success of a transparency artifact, the ability to identify the information decision-makers use and the establishment of processes and guidance needed to acquire that information. We started to explore this idea in our paper with three “scaffolding” frameworks designed to adapt Data Cards to a variety of datasets and organizational contexts. These frameworks helped us create boundary infrastructures, which are the processes and engagement models that complement technical and functional infrastructure necessary to communicate information between communities of practice. Boundary infrastructures enable dataset stakeholders to find common ground used to provide diverse input into decisions for the creation, documentation, and use of datasets.

Today, we introduce the Data Cards Playbook, a self-guided toolkit for a variety of teams to navigate transparency challenges with their ML datasets. The Playbook applies a human-centered design approach to documentation — from planning a transparency strategy and defining the audience to writing reader-centric summaries of complex datasets — to ensure that the usability and utility of the documented datasets are well understood. We’ve created participatory activities to navigate typical obstacles in setting up a dataset transparency effort, frameworks that can scale data transparency to new data types, and guidance that researchers, product teams and companies can use to produce Data Cards that reflect their organizational principles.

The Data Cards Playbook incorporates the latest in fairness, accountability, and transparency research.

The Data Cards Playbook

We created the Playbook using a multi-pronged approach that included surveys, artifact analysis, interviews, and workshops. We studied what Googlers wanted to know about datasets and models, and how they used that information in their day-to-day work. Over the past two years, we deployed templates for transparency artifacts used by fifteen teams at Google, and when bottlenecks arose, we partnered with these teams to determine appropriate workarounds. We then created over twenty Data Cards that describe image, language, tabular, video, audio, and relational datasets in production settings, some of which are now available on GitHub. This multi-faceted approach provided insights into the documentation workflows, collaborative information-gathering practices, information requests from downstream stakeholders, and review and assessment practices for each Google team.

Moreover, we spoke with design, policy, and technology experts across the industry and academia to get their unique feedback on the Data Cards we created. We also incorporated our learnings from a series of workshops at ACM FAccT in 2021. Within Google, we evaluated the effectiveness and scalability of our solutions with ML researchers, data scientists, engineers, AI ethics reviewers, product managers, and leadership. In the Data Cards Playbook, we’ve translated successful approaches into repeatable practices that can easily be adapted to unique team needs.

Activities, Foundations, and Transparency Patterns

The Data Cards Playbook is modeled after sprints and co-design practices, so cross-functional teams and their stakeholders can work together to define transparency with an eye for real-world problems they experience when creating dataset documentation and governance solutions. The thirty-three available Activities invite broad, critical perspectives from a wide variety of stakeholders, so Data Cards can be useful for decisions across the dataset lifecycle. We partnered with researchers from the Responsible AI team at Google to create activities that can reflect considerations of fairness and accountability. For example, we’ve adapted Evaluation Gaps in ML practices into a worksheet for more complete dataset documentation.

Download readily-available activity templates to use the Data Cards Playbook in your organization.

We’ve formed Transparency Patterns with evidence-based guidance to help anticipate challenges faced when producing transparent documentation, offer best practices that improve transparency, and make Data Cards useful for readers from different backgrounds. The challenges and their workarounds are based on data and insights from Googlers, industry experts, and academic research.

Patterns help unblock teams with recommended practices, caution against common pitfalls, and suggested alternatives to roadblocks.

The Playbook also includes Foundations, which are scalable concepts and frameworks that explore fundamental aspects of transparency as new contexts of data modalities and ML arise. Each Foundation supports different product development stages and includes key takeaways, actions for teams, and handy resources.

Playbook Modules

The Playbook is organized into four modules: (1) Ask, (2) Inspect, (3) Answer, and (3) Audit. Each module contains a growing compendium of materials teams can use within their workflows to tackle transparency challenges that frequently co-occur. Since Data Cards were created with scalability and extensibility in mind, modules leverage divergence-converge thinking that teams may already use, so documentation isn’t an afterthought. The Ask and Inspect modules help create and evaluate Data Card templates for organizational needs and principles. The Answer and Audit modules help data teams complete the templates and evaluate the resulting Data Cards.

In Ask, teams define transparency and optimize their dataset documentation for cross-functional decision-making. Participatory activities create opportunities for Data Card readers to have a say in what constitutes transparency in the dataset’s documentation. These address specific challenges and are rated for different intensities and durations so teams can mix-and-match activities around their needs.

The Inspect module contains activities to identify gaps and opportunities in dataset transparency and processes from user-centric and dataset-centric perspectives. It supports teams in refining, validating, and operationalizing Data Card templates across an organization so readers can arrive at reasonable conclusions about the datasets described.

The Answer module contains transparency patterns and dataset-exploration activities to answer challenging and ambiguous questions. Topics covered include preparing for transparency, writing reader-centric summaries in documentation, unpacking the usability and utility of datasets, and maintaining a Data Card over time.

The Audit module helps data teams and organizations set up processes to evaluate completed Data Cards before they are published. It also contains guidance to measure and track how a transparency effort for multiple datasets scales within organizations.

In Practice

A data operations team at Google used an early version of the Lenses and Scopes Activities from the Ask modules to create a customized Data Card template. Interestingly, we saw them use this template across their workflow till datasets were handed off. They used Data Cards to take dataset requests from research teams, tracked the various processes to create the datasets, collected metadata from vendors responsible for annotations, and managed approvals. Their experiences of iterating with experts and managing updates are reflected in our Transparency Patterns.

Another data governance group used a more advanced version of the activities to interview stakeholders for their ML health-related initiative. Using these descriptions, they identified stakeholders to co-create their Data Card schema. Voting on Lenses was used to rule out typical documentation questions, and identify atypical documentation needs specific to their data type, and important for decisions frequently made by ML leadership and tactical roles within their team. These questions were then used to customize existing metadata schemas in their data repositories.

Conclusion

We present the Data Cards Playbook, a continuous and contextual approach to dataset transparency that deliberately considers all relevant materials and contexts. With this, we hope to establish and promote practice-oriented foundations for transparency to pave the path for researchers to develop ML systems and datasets that are responsible and benefit society.

In addition to the four Playbook modules described, we’re also open-sourcing a card builder, which generates interactive Data Cards from a Markdown file. You can see the builder in action in the GEM Benchmark project’s Data Cards. The Data Cards created were a result of activities from this Playbook, in which the GEM team identified improvements across all dimensions, and created an interactive collection tool designed around scopes.

We acknowledge that this is not a comprehensive solution for fairness, accountability, or transparency in itself. We’ll continue to improve the Playbook using lessons learned. We hope the Data Cards Playbook can become a robust platform for collaboratively advancing transparency research, and invite you to make this your own.

Acknowledgements

This work was done in collaboration with Reena Jana, Vivian Tsai, and Oddur Kjartansson. We want to thank Donald Gonzalez, Dan Nanas, Parker Barnes, Laura Rosenstein, Diana Akrong, Monica Caraway, Ding Wang, Danielle Smalls, Aybuke Turker, Emily Brouillet, Andrew Fuchs, Sebastian Gehrmann, Cassie Kozyrkov, Alex Siegman, and Anthony Keene for their immense contributions; and Meg Mitchell and Timnit Gebru for championing this work.

We also want to thank Adam Boulanger, Lauren Wilcox, Roxanne Pinto, Parker Barnes, and Ayça Çakmakli for their feedback; Tulsee Doshi, Dan Liebling, Meredith Morris, Lucas Dixon, Fernanda Viegas, Jen Gennai, and Marian Croak for their support. This work would not have been possible without our workshop and study participants, and numerous partners, whose insights and experiences have shaped this Playbook.