Adobe, Inc.

International Business Machines Corporation

Sebastian Gehrmann

Franck Dernoncourt

Payel Das

Carl Dockhorn

Cicero Nogueira Dos Santos

Inkit Padhi

Lidan Wang

Kahini Wadhawan

Yu Gong

Tom D J Sercu

A computer-implemented method according to one aspect includes training a latent variable model (LVM), utilizing labeled data and unlabeled data within a data set; training a classifier, utilizing the labeled data and associated labels within the data set; and generating new data having a predetermined set of labels, utilizing the trained LVM and the trained classifier.

Generating attribute-based samples

Embodiments of the present invention provide systems, methods, and computer storage media for sentence compression in which a provided sentence is compressed to fit within an allotted space. Portions of the input sentence are copied to generate the compressed sentence. Upon receipt of a sentence, top candidate compressed sentences may be determined based on probabilities of segments of the input sentence to be included in a potential compressed sentence. The top candidate compressed sentences are re-ranked based on grammatical accuracy scores for each of the candidate compressed sentences using a language model trained using linguistic features of words and/or phrases. The highest scoring candidate compressed sentence may be presented to the user.

Low-resource sentence compression system

Certain embodiments involve a method for generating a summary. The method includes one or more processing devices performing operations including generating a set of word embeddings corresponding to each word of a text input. The operations further include generating a set of selection probabilities corresponding to each word of the text input using the respective word embeddings. Further, the operations include calculating a set of sentence saliency scores for a set of sentences of the text input using respective selection probabilities of the set of selection probabilities for each word of the text input. Additional, the operations include generating the summary of the text input using a subset of sentences from the set of sentences with greatest sentence saliency scores from the set of sentence saliency scores.

Generating summary content using supervised sentential extractive summarization

The disclosure describes one or more embodiments of a structured text summary system that generates structured text summaries of digital documents based on an interactive graphical user interface. For example, the structured text summary system can collaborate with users to create structured text summaries of a digital document based on automatically generating document tags corresponding to the digital document, determining segments of the digital document that correspond to a selected document tag, and generating structured text summaries for those document segments.

Generating structured text summaries of digital documents using interactive collaboration

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Cambridge, MA, United States of America

Sebastian Gehrmann