Due to the advent of social media and web 2.0, we are faced with a deluge of information; recently, research efforts have focused on filtering out noisy, irrelevant information items from social media streams and in particular have attempted to automatically identify and summarise events. However, due to the heterogeneous nature of such social media streams, these efforts have not reached fruition. In this paper, we investigate how images can be used as a source for summarising events. Existing approaches have considered only textual summaries which are often poorly written, in a different language and slow to digest. Alternatively, images are "worth 1,000 words" and are able to quickly and easily convey an idea or scene. Since images in social media can also be noisy, irrelevant and repetitive, we propose new techniques for their automatic selection, ranking and presentation. We evaluate our approach on a recently created social media event data set containing 365k tweets and 50 events, for which we extend by collecting 625k related images. By conducting two crowdsourced evaluations, we firstly show how our approach overcomes the problems of automatically collecting relevant and diverse images from noisy microblog data, before highlighting the advantages of multimedia summarisation over text based approaches
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