Abstract: Buying stage determination techniques in a digital medium environment are described to model and control user interaction with a service provider to purchase a good or service through identification of stages in a buying cycle associated with users. Usage data is obtained that at least describes interactions by one or more users with the service provider that occurred during previous sessions. The marketing interactions in the obtained usage data are classified and quantified for respective said users using one or more features. Generation of a model of the stages of the buying cycle is controlled using the quantified usage data. The model is configured to identify a stage in the buying cycle that is associated with a respective said user, which is usable to control further marketing interactions supported for that user.

Abstract: Multimedia document summarization techniques are described. That is, given a document that includes text and a set of images, various implementations generate a summary by extracting relevant text segments in the document and relevant segments of images with constraints on the amount of text and number/size of images in the summary.

Abstract: Systems and methods disclosed select and display icons corresponding to topics of digital documents. In particular, in one or more embodiments, systems and methods identify similar clusters of documents from a plurality of documents and generate icons for each identified cluster of documents. For instance, systems and methods identify topical phrases from identified clusters and utilize the topical phrases to identify icons corresponding to the topical phrases. More specifically, one or more embodiments include systems and methods that generate generalized terms from topical phrases utilizing a concept taxonomy and then identify icons by comparing the generated generalized terms to tags corresponding to a plurality of icons.

Abstract: A quality metric of a multimedia summary of a multimedia content item is determined based, in part, on semantic similarities of the summary and content item, rather than just on word frequencies. This is accomplished in some embodiments by identifying a semantic meaning of the summary and multimedia content item using vector analysis. The vectors of the summary and the vectors of the multimedia content item are compared to determine semantic similarity. In other examples, the quality metric of the multimedia summary is determined based on, in part, a coherence between an image portion of a summary and a text portion of the summary for determining a quality metric of a multimedia summary.