Abstract: Techniques are provided herein for generating improved document summaries that consider the amount of time that has passed since the user last accessed the document. The length of time that has passed since the user has accessed each previous portion of the document is used as a variable to determine how much the summary should focus on each of the previously read sections of the document. When a document is accessed by a user, a relevance score is assigned to content from previously accessed sections of that document, where the relevance score is weighted based on how long ago each of the sections was accessed by the user. Once the various content items of previous sections have been provided relevance scores, selected sentences with the highest relevance scores are fed to a deep learning sequence-to-sequence model is used to build the document summary.

Abstract: Techniques are provided herein for generating improved document summaries that consider the amount of time that has passed since the user last accessed the document. The length of time that has passed since the user has accessed each previous portion of the document is used as a variable to determine how much the summary should focus on each of the previously read sections of the document. When a document is accessed by a user, a relevance score is assigned to content from previously accessed sections of that document, where the relevance score is weighted based on how long ago each of the sections was accessed by the user. Once the various content items of previous sections have been provided relevance scores, selected sentences with the highest relevance scores are fed to a deep learning sequence-to-sequence model is used to build the document summary.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for techniques for identifying textual similarity and performing answer selection. A textual-similarity computing model can use a pre-trained language model to generate vector representations of a question and a candidate answer from a target corpus. The target corpus can be clustered into latent topics (or other latent groupings), and probabilities of a question or candidate answer being in each of the latent topics can be calculated and condensed (e.g., downsampled) to improve performance and focus on the most relevant topics. The condensed probabilities can be aggregated and combined with a downstream vector representation of the question (or answer) so the model can use focused topical and other categorical information as auxiliary information in a similarity computation.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for techniques for identifying textual similarity and performing answer selection. A textual-similarity computing model can use a pre-trained language model to generate vector representations of a question and a candidate answer from a target corpus. The target corpus can be clustered into latent topics (or other latent groupings), and probabilities of a question or candidate answer being in each of the latent topics can be calculated and condensed (e.g., downsampled) to improve performance and focus on the most relevant topics. The condensed probabilities can be aggregated and combined with a downstream vector representation of the question (or answer) so the model can use focused topical and other categorical information as auxiliary information in a similarity computation.

Abstract: Techniques are disclosed for generating a structured transcription from a speech file. In an example embodiment, a structured transcription system receives a speech file comprising speech from one or more people and generates a navigable structured transcription object. The navigable structured transcription object may comprise one or more data structures representing multimedia content with which a user may navigate and interact via a user interface. Text and/or speech relating to the speech file can be selectively presented to the user (e.g., the text can be presented via a display, and the speech can be aurally presented via a speaker).

Abstract: Highlighting key portions of text within a document is described. A document having text is obtained, and key portions of the document are determined using summarization techniques. Key portion data indicative of the key portions is generated and maintained for output to generate a highlighted document in which highlight overlays are displayed over or proximate the determined key portions of the text within the document. In one or more implementations, reader interactions with the highlighted document are monitored to generate reader feedback data. The reader feedback data may then be combined with the output of the summarization techniques in order to adjust the determined key portions. In some cases, the reader feedback data may also be used to improve the summarization techniques.

Abstract: Techniques are disclosed for generating a structured transcription from a speech file. In an example embodiment, a structured transcription system receives a speech file comprising speech from one or more people and generates a navigable structured transcription object. The navigable structured transcription object may comprise one or more data structures representing multimedia content with which a user may navigate and interact via a user interface. Text and/or speech relating to the speech file can be selectively presented to the user (e.g., the text can be presented via a display, and the speech can be aurally presented via a speaker).

Abstract: Highlighting key portions of text within a document is described. A document having text is obtained, and key portions of the document are determined using summarization techniques. Key portion data indicative of the key portions is generated and maintained for output to generate a highlighted document in which highlight overlays are displayed over or proximate the determined key portions of the text within the document. In one or more implementations, reader interactions with the highlighted document are monitored to generate reader feedback data. The reader feedback data may then be combined with the output of the summarization techniques in order to adjust the determined key portions. In some cases, the reader feedback data may also be used to improve the summarization techniques.

Abstract: Highlighting key portions of text within a document is described. A document having text is obtained, and key portions of the document are determined using summarization techniques. Key portion data indicative of the key portions is generated and maintained for output to generate a highlighted document in which highlight overlays are displayed over or proximate the determined key portions of the text within the document. In one or more implementations, reader interactions with the highlighted document are monitored to generate reader feedback data. The reader feedback data may then be combined with the output of the summarization techniques in order to adjust the determined key portions. In some cases, the reader feedback data may also be used to improve the summarization techniques.