Smart To-Do: Automatic Generation of To-Do Items from Emails (2020)

Abstract: Intelligent features in email service applications aim to increase productivity by helping people organize their folders, compose their emails and respond to pending tasks. In this work, we explore a new application, Smart-To-Do, that helps users with task management over emails. We introduce a new task and dataset for automatically generating To-Do items from emails where the sender has promised to perform an action. We design a two-stage process leveraging recent advances in neural text generation and sequence-to-sequence learning, obtaining BLEU and ROUGE scores of 0.23 and 0.63 for this task. To the best of our knowledge, this is the first work to address the problem of composing To-Do items from emails.

## introduction email is one of the most used forms of communication especially in enterprise and work settings @xcite . with the growing number of users in email platforms, service providers are constantly seeking to improve user experience for a myriad of applications such as online retail, instant messaging and event management @xcite . smart reply @xcite and smart compose @xcite are two recent features that provide contextual assistance to users aiming to reduce typing efforts. another line of work in this direction is for automated task management and scheduling. for example. the recent nudge feature 1 in gmail and insights in outlook 2 are designed to remind users to follow-up on an email or pay attention to pending tasks. smart to-do takes a step further in task assistance and seeks to boost user productivity by automatically generating to-do items from their email context. text generation from emails, like creating to-do items, is replete with complexities due to the diversity of conversations in email threads, heterogeneous structure of emails and various meta-deta involved. as opposed to prior works in text generation like news headlines, email subject lines and email conversation summarization, to-do items are action-focused, requiring the identification of a specific task to be performed. in this work, we introduce the task of automatically generating to-do items from email context and meta-data to assist users with following up on their promised actions (also referred to as commitments in this work). refer to figure 1 for an illustration. given an email, its temporal context (i.e. thread), and associated meta-data like the name of the sender and recipient, we want to generate a short and succinct to-do item for the task mentioned in the email. this requires identifying the task sentence (also referred to as a query), relevant sentences in the email that provide contextual information about the query along with the entities (e.g., people) associated with the task. we utilize existing work to identify the task sentence via a commitment classifier that detects action intents in the emails. thereafter we use an unsupervised technique to extract key sentences in the email that are helpful in providing contextual information about the query. these pieces of information are further combined to generate the to-do item using a sequence-to-sequence architecture with deep neural networks. figure 2 shows a schematic diagram of the process. since there is no existing work or dataset on this problem, our first step is to collect annotated data for this task. overall, our contributions can be summarized as follows: ## dataset preparation we build upon the avocado dataset @xcite foot_0 containing an anonymized version of the outlook mailbox for 279 employees with various meta-data and 938, 035 emails overall. ## smart to-do : two stage generation in this section, we describe our two-stage approach to generate to-do items. in the first stage, we select sentences that are helpful in writing the to-do item. emails contain generic sentences such as salutations, thanks and casual conversations not relevant to the commitment task. the objective of the first stage is to select sentences containing informative concepts necessary to write the to-do. ## experimental results we trained the above neural networks for to-do item generation on our annotated dataset. of the 9349 email instances with to-do items, we used 7349 for training and 1000 each for validation and testing. for each instance, we chose the annotation with fewer tokens as ground-truth reference. the median token length of the encoder input is 43 (including the helpful sentence). a trivial baseline -which concatenates tokens from the 'sent-to' and 'subject' fields and the commitment sentence -is included for comparison. the best performance is obtained with seq2seq using copying mechanism. we observe our model to perform at par with human performance for writing to-do items. ## conclusions in this work, we study the problem of automatic to-do item generation from email context and metadata to provide smart contextual assistance in email applications. to this end, we introduce a new task and dataset for action-focused text intelligence. we design a two stage framework with deep neural networks for task-focused text generation. there are several directions for future work including better architecture design for utilizing structured meta-data and replacing the two-stage framework with a multi-task generation model that can jointly identify helpful context for the task and perform corresponding text generation.