Keyword extraction is an integral task for many downstream problems like clustering, recommendation, search and classification. Development and evaluation of keyword extraction techniques require an exhaustive dataset; however, currently, the community lacks large-scale multi-lingual datasets. In this paper, we present MAKED, a large-scale multi-lingual keyword extraction dataset comprising of 540K+ news articles from British Broadcasting Corporation News (BBC News) spanning 20 languages. It is the first keyword extraction dataset for 11 of these 20 languages. The quality of the dataset is examined by experimentation with several baselines. We believe that the proposed dataset will help advance the field of automatic keyword extraction given its size, diversity in terms of languages used, topics covered and time periods as well as its focus on under-studied languages.

Multimodal Summarization (MS) has attracted research interest in the past few years due to the ease with which users perceive multimodal summaries. It is important for MS models to consider the topic a given target content belongs to. In the current paper, we propose a topic-aware MS system which performs two tasks simultaneously: differentiating the images into “on-topic” and “off-topic” categories and further utilizing the “on-topic” images to generate multimodal summaries. The hypothesis is that, the proposed topic similarity classifier will help in generating better multimodal summary by focusing on important components of images and text which are specific to a particular topic. To develop the topic similarity classifier, we have augmented the existing popular MS data set, MSMO, with similar “on-topic” and dissimilar “off-topic” images for each sample. Our experimental results establish that the focus on “on-topic” features helps in generating topic-aware multimodal summaries, which outperforms the state of the art approach by 1.7 % in ROUGE-L metric.

Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a source sentence into the target style. To tackle these problems, an intermediate representation is often constructed that is devoid of style while still preserving the meaning of the source sentence. In this work, we study the usefulness of Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation. We posit that semantic notations like AMR are a natural choice for an intermediate representation. Hence, we propose T-STAR: a model comprising of two components, text-to-AMR encoder and a AMR-to-text decoder. We propose several modeling improvements to enhance the style agnosticity of the generated AMR. To the best of our knowledge, T-STAR is the first work that uses AMR as an intermediate representation for TST. With thorough experimental evaluation we show T-STAR significantly outperforms state of the art techniques by achieving on an average 15.2% higher content preservation with negligible loss (~3%) in style accuracy. Through detailed human evaluation with 90,000 ratings, we also show that T-STAR has upto 50% lesser hallucinations compared to state of the art TST models.

The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploits semantic overlap as opposed to its predecessors that focus more on syntactic information overlap. Our model outperforms the state-of-the-art baselines in terms of ROUGE, METEOR and word mover similarity (WMS), establishing the superiority of the proposed system via extensive ablation experiments. We have also challenged the summarization capabilities of the state of the art Pointer Generator Network (PGN), and through thorough experimentation, shown that PGN is more of a paraphraser, contrary to the prevailing notion of a summarizer; illustrating it’s incapability to accumulate information across multiple sentences.