text: We present Semantic WordRank (SWR), an unsupervised method for generating an extractive summary of a single document. Built on a weighted word graph with semantic and co-occurrence edges, SWR scores sentences using an article-structure-biased PageRank algorithm with a Softplus function adjustment, and promotes topic diversity using spectral subtopic clustering under the Word-Movers-Distance metric. We evaluate SWR on the DUC-02 and SummBank datasets and show that SWR produces better summaries than the state-of-the-art algorithms over DUC-02 under common ROUGE measures. We then show that, under the same measures over SummBank, SWR outperforms each of the three human annotators (aka. judges) and compares favorably with the combined performance of all judges.
  @cite_22: We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.
  @cite_21: Extractive summarization aims to generate a summary by ranking sentences, whose performance relies heavily on the quality of sentence features. In this paper, a document summarization framework based on convolutional neural networks is successfully developed to learn sentence features and perform sentence ranking jointly. We adapt the original CNN model to address a regression process for sentence ranking. Pre-trained word vectors are used to enhance the performance of our model. We evaluate our proposed method on the DUC 2002 and 2004 datasets covering single and multi-document summarization tasks respectively. The proposed system achieves competitive or even better performance compared with state-of-the-art document summarization systems.
  @cite_7: We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.
  @cite_12: 
summary: Deep learning methods, able to learn sentence or document representations automatically, have recently been used to score sentences. For example, R2N2 @cite_12 uses a recursive neural network for both word level and sentence level scoring, followed by an ILP optimization strategy for selecting sentences. CNN-W2V @cite_21 is another example, which modifies a convolutional-neural-network (CNN) model of sentence classification @cite_7 to rank sentences. SummaRuNNer @cite_22 , on the other hand, treats summarizations as sequence classifications and uses a two-layer bi-directional recurrent neural network (RNN) model to extract sentences, where the first layer RNN is for words and the second layer is for sentences. Unlike unsupervised methods, the state-of-the-art deep learning approaches require a larger dataset and a significantly longer time to train a model, yet with a much lower ROUGE-1 score when evaluating on DUC dataset.