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With an increasing number of new summarization systems proposed in recent years, an automatic text evaluation metric that can accurately and reliably rate the performance of summarization systems has been a pressing need. However, current automatic text evaluation metrics can only measure one or certain aspects of the quality between two summary texts and do not agree with human judgments consistently. In this paper, we show that combining multiple well-chosen evaluation metrics and training predictive models using human annotated datasets can lead to more reliable evaluation scores than using any individual automatic metric. Our predictive models trained on a human annotated subset of the CNN/DailyMail corpus demonstrate significant improvements (e.g. approximately 25% along coherence dimension) over selected individual metrics. Furthermore, a concise meta-evaluation on automatic metrics is provided along with an analysis of the performance of 12 predictive models. We also investigate the sensitivity of automatic metrics when mixed together for training these models. We have made the code, the instructions for experiment setup, and the trained models available as a tool for comparing and evaluating text summarization systems. a https://github.com/bzhao2718/ReliableSummEvalReg. [ABSTRACT FROM AUTHOR]

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI), results in merely a few hundred training documents. [ABSTRACT FROM AUTHOR]

In this paper, we propose DeepSumm, a novel method based on topic modeling and word embeddings for the extractive summarization of single documents. Recent summarization methods based on sequence networks fail to capture the long range semantics of the document which are encapsulated in the topic vectors of the document. In DeepSumm, our aim is to utilize the latent information in the document estimated via topic vectors and sequence networks to improve the quality and accuracy of the summarized text. Each sentence is encoded through two different recurrent neural networks based on probabilistic topic distributions and word embeddings, and then a sequence to sequence network is applied to each sentence encoding. The outputs of the encoder and the decoder in the sequence to sequence networks are combined after weighting using an attention mechanism and converted into a score through a multi-layer perceptron network. We refer to the score obtained through the topic model as Sentence Topic Score (STS) and to the score generated through word embeddings as Sentence Content Score (SCS). In addition, we propose Sentence Novelty Score (SNS) and Sentence Position Score (SPS) and perform a weighted fusion of the four scores for each sentence in the document to compute a Final Sentence Score (FSS). The proposed DeepSumm framework was evaluated on the standard DUC 2002 benchmark and CNN/DailyMail datasets. Experimentally, it was demonstrated that our method captures both the global and the local semantic information of the document and essentially outperforms existing state-of-the-art approaches for extractive text summarization with ROUGE-1, ROUGE-2, and ROUGE-L scores of 53.2, 28.7 and 49.2 on DUC 2002 and 43.3, 19.0 and 38.9 on CNN/DailyMail dataset. • DeepSumm for extractive summarization based on seq2seq networks. • Sentence encoded with probabilistic topic distributions and word embeddings RNNs. • Proposed Sentence Novelty Score, based on sentence content and topic embeddings. • Seq2seq attention networks extract content and topic Scores. • Summary generated using four sentence scores: Content, Topic, Novelty and Position. [ABSTRACT FROM AUTHOR]

Select-then-compress is a popular hybrid, framework for text summarization due to its high efficiency. This framework first selects salient sentences and then independently condenses each of the selected sentences into a concise version. However, compressing sentences separately ignores the context information of the document, and is therefore prone to delete salient information. To address this limitation, we propose a novel condense-then-select framework for text summarization. Our framework first concurrently condenses each document sentence. Original document sentences and their compressed versions then become the candidates for extraction. Finally, an extractor utilizes the context information of the document to select candidates and assembles them into a summary. If salient information is deleted during condensing, the extractor can select an original sentence to retain the information. Thus, our framework helps to avoid the loss of salient information, while preserving the high efficiency of sentence-level compression. Experiment results 1 1 Code will be available at https://github.com/kenchan0226/abs-then-ext-public. on the CNN/DailyMail, DUC-2002, and Pubmed datasets demonstrate that our framework outperforms the select-then-compress framework and other strong baselines. [ABSTRACT FROM AUTHOR]