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We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach.We explore both recurrent neural network (RNN) and Transformer-based sequence-to-sequence architectures for summarizing medical conversations. We have incorporated enhancements to these architectures, such as the pointer-generator network that facilitates copying parts of the conversations to the reports, and a hierarchical RNN encoder that makes RNN training three times faster with long inputs. A comparison of the relative improvements from the different model architectures over an oracle extractive baseline is provided on a dataset of 800k orthopedic encounters. Consistent with observations in literature for machine translation and related tasks, we find the Transformer models outperform RNN in accuracy, while taking less than half the time to train. Significantly large wins over a strong oracle baseline indicate that sequenceto-sequence modeling is a promising approach for automatic generation of medical reports, in the presence of data at scale.

How can we cull the facts we need from the overwhelming mass of information and misinformation that is the Web? The TextRunner extraction engine represents one approach, in which people pose keyword queries or simple questions and TextRunner returns concise answers based on tuples extracted from Web text. Unfortunately, the results returned by engines such as TextRunner include both informative facts (e.g., "the FDA banned ephedra") and less useful statements (e.g., "the FDA banned products"). This paper therefore investigates filtering TextRunner results to enable people to better focus on interesting assertions. We first develop three distinct models of what assertions are likely to be interesting in response to a query. We then fully operationalize each of these models as a filter over TextRunner results. Finally, we develop a more sophisticated filter that combines the different models using relevance feedback. In a study of human ratings of the interestingness of TextRunner assertions, we show that our approach substantially enhances the quality of TextRunner results. Our best filter raises the fraction of interesting results in the top thirty from 41.6% to 64.1%.

We introduce an entity-centric search experience, called Active Objects, in which entity-bearing queries are paired with actions that can be performed on the entities. For example, given a query for a specific flashlight, we aim to present actions such as reading reviews, watching demo videos, and finding the best price online.In an annotation study conducted over a random sample of user query sessions, we found that a large proportion of queries in query logs involve actions on entities, calling for an automatic approach to identifying relevant actions for entity-bearing queries. In this paper, we pose the problem of finding actions that can be performed on entities as the problem of probabilistic inference in a graphical model that captures how an entity bearing query is generated. We design models of increasing complexity that capture latent factors such as entity type and intended actions that determine how a user writes a query in a search box, and the URL that they click on. Given a large collection of real-world queries and clicks from a commercial search engine, the models are learned efficiently through maximum likelihood estimation using an EM algorithm. Given a new query, probabilistic inference enables recommendation of a set of pertinent actions and hosts. We propose an evaluation methodology for measuring the relevance of our recommended actions, and show empirical evidence of the quality and the diversity of the discovered actions.

Today's Web browsers allow users to open links in new windows or tabs. This action, which we call 'branching', is sometimes performed on search results when the user plans to eventually visit multiple results. We detect branching behavior on a large commercial search engine with a client-side script on the results page. Two-fifths of all users spawned new tabs on search results in the timeframe of our study; branching usage varied with different query types and vertical. Both branching and backtracking are viable methods for visiting multiple search results. To understand user search strategies, we treat multiple result clicks following a query as ordered events to understand user search strategies. Users branching in a query are more likely to click search results from top to bottom, while users who backtrack are less likely to do so; this is especially true for queries involving more than two clicks. These findings inform an experiment in which we take a popular click model and modify it to account for the differing user behavior when branching. By understanding that users continue examining search results before viewing a branched result, we can improve the click model for branching queries.