Abstract: The disclosure herein describes training a text processing model to generate model output text data using input text data and a sentence count. A training data entry including input text data and output text data is obtained. A sentence count of the output text data is determined, and the output text data is labeled with a sentence count label and a sentence number label. Model output text data is generated with a text processing model using the input text data and determined sentence count as input data. Loss data associated with a difference between the generated model output text data and the labeled output text data is determined and the text processing model is adjusted using the determined loss data. The use of labeled output text data enables the model to be trained to produce output text data with a target sentence count in a computationally efficient manner.

Abstract: Example solutions for image paragraph captioning use a first vision language model to generate visual information (comprising text) for an image. The visual information may include tags, an initial image caption, and information on objects within the image (e.g., further tags and captions, and object attributes and locations within the image). In some examples, the visual information further includes visual clues. A generative language model generates a plurality of image story caption candidates (e.g., descriptive paragraphs) from the visual information. A second vision language model evaluates the plurality of image story caption candidates and selects a caption as the final output caption.