Abstract: Implementations relate to an application that can bias automatic speech recognition for meetings using data that may be associated with the meeting and/or meeting participants. A transcription of inputs provided during a meeting can additionally and/or alternatively be processed to determine whether the inputs should be incorporated into a meeting document, which can provide a summary for the meeting. In some instances, entries into a meeting document can be designated as action items, and those action items can optionally have conditions for reminding meeting participants about the action items and/or for determining whether an action item has been fulfilled. In this way, various tasks that may typically be manually performed by meeting participants, such as creating a meeting summary, can be automated in a more accurate manner. This can preserve resources that may otherwise be wasted during video conferences, in-person meetings, and/or other gatherings.

Abstract: A method includes obtaining a transcript of multiple dialog acts during a multi-party communication session and extracting, from the transcript, a plurality of extractive action items each associated with a task expected to be completed within a timeframe to begin after the multi-party communication session ends. The method also includes generating, using an abstractive action item identification model configured to receive the extractive action items extracted from the transcript, one or more abstractive action items. Each abstractive action item is associated with a respective group of one or more of the extractive action items that are associated with a same respective task. For each of the one or more abstractive action items, the method also includes presenting, in one or more graphical user interfaces, information related to the corresponding abstractive action item.

Abstract: An improved system and method using text features for click prediction of sponsored search advertising is provided. A maximum entropy click prediction model that predicts the click probability of query-advertisement pairs may be generated from click feedback features and word pair features of a query and an advertisement. The maximum entropy click prediction model may be used to obtain click probabilities for query-advertisement pairs to determine and serve a ranked list of advertisements for display with query results in an online keyword search auction. A search query may be received and word features from the search query may be input into the maximum entropy click prediction model to obtain click probabilities for query-advertisement pairs. A list of advertisements may be ranked using click probabilities for query-advertisement pairs, the list of ranked advertisements may be priced in an online search keyword auction and served for display with search query results.