5,145 research outputs found
Self-Supervised and Controlled Multi-Document Opinion Summarization
We address the problem of unsupervised abstractive summarization of
collections of user generated reviews with self-supervision and control. We
propose a self-supervised setup that considers an individual document as a
target summary for a set of similar documents. This setting makes training
simpler than previous approaches by relying only on standard log-likelihood
loss. We address the problem of hallucinations through the use of control
codes, to steer the generation towards more coherent and relevant
summaries.Finally, we extend the Transformer architecture to allow for multiple
reviews as input. Our benchmarks on two datasets against graph-based and recent
neural abstractive unsupervised models show that our proposed method generates
summaries with a superior quality and relevance.This is confirmed in our human
evaluation which focuses explicitly on the faithfulness of generated summaries
We also provide an ablation study, which shows the importance of the control
setup in controlling hallucinations and achieve high sentiment and topic
alignment of the summaries with the input reviews.Comment: 18 pages including 5 pages appendi
Deep attentive video summarization with distribution consistency learning
This article studies supervised video summarization by formulating it into a sequence-to-sequence learning framework, in which the input and output are sequences of original video frames and their predicted importance scores, respectively. Two critical issues are addressed in this article: short-term contextual attention insufficiency and distribution inconsistency. The former lies in the insufficiency of capturing the short-term contextual attention information within the video sequence itself since the existing approaches focus a lot on the long-term encoder-decoder attention. The latter refers to the distributions of predicted importance score sequence and the ground-truth sequence is inconsistent, which may lead to a suboptimal solution. To better mitigate the first issue, we incorporate a self-attention mechanism in the encoder to highlight the important keyframes in a short-term context. The proposed approach alongside the encoder-decoder attention constitutes our deep attentive models for video summarization. For the second one, we propose a distribution consistency learning method by employing a simple yet effective regularization loss term, which seeks a consistent distribution for the two sequences. Our final approach is dubbed as Attentive and Distribution consistent video Summarization (ADSum). Extensive experiments on benchmark data sets demonstrate the superiority of the proposed ADSum approach against state-of-the-art approaches
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