AUTOMATIC CAPTION GENERATION FOR NEWS IMAGES USING EXTRACTIVE AND ABSTRACTIVE MODELS

Automatic image caption generation is of great interest to many image related applications. Now a day’s, whenever retrieving images from the search Engines that retrieves images without analyzing the ir content, simply by matching user queries against the image’s file name and format, user - annotated tags, captions, and, generally, text surrounding the image. Also the retrieved image does not contain any textual data along with the images. We introduced the task of automatic caption generation for news images. The task fuses insights from computer vision and natural language processing and holds promise for various multimedia applications, such as image retrieval, development of tools supporting news med ia management, and for individuals with visual impairment. It is possible to learn a caption generation model from weakly labelled data without costly manual involvement. Instead of manually creating annotations, image captions are treated as labels for th e image. Although the caption words are admittedly noisy compared to traditional human - created keywords, we show that they can be used to learn the correspondences between visual and textual modalities, and also serve as a gold standard for the caption gen eration task. We have presented extractive and abstractive caption generation models. A key aspect of our approach is to allow both the visual and textual modalities to influence the generation task.