Rethinking summarization and storytelling for modern social multimedia

Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the context of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to re-focus the problem space in order to meet the information needs in the age of user-generated content in different formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanatio

Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention

We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched. Our method explicitly leverages the contents of both the preferred clips and the target videos using pre-trained features for the objects and the human activities. We design a multi-head attention mechanism to adaptively weigh the preferred clips based on their object- and human-activity-based contents, and fuse them using these weights into a single feature representation for each user. We compute similarities between these per-user feature representations and the per-frame features computed from the desired target videos to estimate the user-specific highlight clips from the target videos. We test our method on a large-scale highlight detection dataset containing the annotated highlights of individual users. Compared to current baselines, we observe an absolute improvement of 2-4% in the mean average precision of the detected highlights. We also perform extensive ablation experiments on the number of preferred highlight clips associated with each user as well as on the object- and human-activity-based feature representations to validate that our method is indeed both content-based and user-specific.Comment: 14 pages, 5 figures, 7 table

Crowdsourced intuitive visual design feedback

For many people images are a medium preferable to text and yet, with the exception of star ratings, most formats for conventional computer mediated feedback focus on text. This thesis develops a new method of crowd feedback for designers based on images. Visual summaries are generated from a crowd’s feedback images chosen in response to a design. The summaries provide the designer with impressionistic and inspiring visual feedback. The thesis sets out the motivation for this new method, describes the development of perceptually organised image sets and a summarisation algorithm to implement it. Evaluation studies are reported which, through a mixed methods approach, provide evidence of the validity and potential of the new image-based feedback method. It is concluded that the visual feedback method would be more appealing than text for that section of the population who may be of a visual cognitive style. Indeed the evaluation studies are evidence that such users believe images are as good as text when communicating their emotional reaction about a design. Designer participants reported being inspired by the visual feedback where, comparably, they were not inspired by text. They also reported that the feedback can represent the perceived mood in their designs, and that they would be enthusiastic users of a service offering this new form of visual design feedback

Identifying landscape relevant natural language using actively crowdsourced landscape descriptions and sentence-transformers

Natural language has proven to be a valuable source of data for various scientific inquiries including landscape perception and preference research. However, large high quality landscape relevant corpora are scare. We here propose and discuss a natural language processing workflow to identify landscape relevant documents in large collections of unstructured text. Using a small curated high quality collection of actively crowdsourced landscape descriptions we identify and extract similar documents from two different corpora (Geograph and WikiHow) using sentence-transformers and cosine similarity scores. We show that 1) sentence-transformers combined with cosine similarity calculations successfully identify similar documents in both Geograph and WikiHow effectively opening the door to the creation of new landscape specific corpora, 2) the proposed sentence-transformer approach outperforms traditional Term Frequency - Inverse Document Frequency based approaches and 3) the identified documents capture similar topics when compared to the original high quality collection. The presented workflow is transferable to various scientific disciplines in need of domain specific natural language corpora as underlying data