Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR , the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets

Summarizing information from Web sites on distributed power generation and alternative energy development

The World Wide Web (WWW) has become a huge repository of information and knowledge, and an essential channel for information exchange. Many sites and thousands of pages of information on distributed power generation and alternate energy development are being added or modified constantly and the task of finding the most appropriate information is getting difficult. While search engines are capable to return a collection of links according to key terms and some forms of ranking mechanism, it is still necessary to access the Web page and navigate through the site in order to find the information. This paper proposes an interactive summarization framework called iWISE to facilitate the process by providing a summary of the information on the Web site. The proposed approach makes use of graphical visualization, tag clouds and text summarization. A number of cases are presented and compared in this paper with a discussion on future work

A web assessment approach based on summarisation and visualisation

The number of Web sites has noticeably increased to roughly 224 million in last ten years. This means there is a rapid growth of information on the Internet. Although search engines can help users to filter their desired information, the searched result is normally presented in the form of a very long list, and users have to visit each Web page in order to determine the appropriateness of the result. This leads to a considerable amount of time has to be spent on finding the required information. To address this issue, this paper proposes a Web assessment approach in order to provide an overview of the information on a Website using an integration of existing summarisation and visualisation techniques, which are text summarisation, tag cloud, Document Type View, and interactive features. This approach is capable to reduce the time required to identify and search for information from the Web

Video summarisation: A conceptual framework and survey of the state of the art

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users