We present a feature vector formation technique for documents -Sparse Composite Document Vector (SCDV) -which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embeddings are clustered to capture multiple semantic contexts in which words occur. They are then chained together to form document topic-vectors that can express complex, multi-topic documents. Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG (Liu et al., 2015a). We also show that SCDV embeddings perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval. Moreover, we achieve significant reduction in training and prediction times compared to other representation methods. SCDV achieves best of both worlds -better performance with lower time and space complexity.
We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.
Most earlier work on text summarization is carried out on news article datasets. The summary in these datasets is naturally located at the beginning of the text. Hence, a model can spuriously utilize this correlation for summary generation instead of truly learning to summarize. To address this issue, we constructed a new dataset, SUMPUBMED, using scientific articles from the PubMed archive. We conducted a human analysis of summary coverage, redundancy, readability, coherence, and informativeness on SUMPUBMED. SUMPUBMED is challenging because (a) the summary is distributed throughout the text (not-localized on top), and (b) it contains rare domain-specific scientific terms. We observe that seq2seq models that adequately summarize news articles struggle to summarize SUMPUBMED. Thus, SUMPUBMED opens new avenues for the future improvement of models as well as the development of new evaluation metrics.
This work aims to provide a page segmentation algorithm which uses both visual and content information to extract the semantic structure of a web page. The visual information is utilized using the VIPS algorithm and the content information using a pre-trained Naive Bayes classifier. The output of the algorithm is a semantic structure tree whose leaves represent segments having unique topic. However contents of the leaf segments may possibly be physically distributed in the web page. This structure can be useful in many web applications like information retrieval, information extraction and automatic web page adaptation. This algorithm is expected to outperform other existing page segmentation algorithms since it utilizes both content and visual information.
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