An ExtractionAbstraction Hybrid Approach for Long Document Summarization In this paper, we propose a hybrid model of extractive and abstractive methods to tackle the long document automatic summarization task. The model first trains an extractor to extract salient sentences from the original text. Next, these salient sentences are put together to get a condensed version of the original text. Then we use the abstractive model to rewrite the extracted sentences to get the final summary. In order to avoid the exposure bias, reinforcement training is used to optimize the proposed model. Experiments in NLPCC2017 Shared Task 3 show that our models achieve competitive performance. Additionally, the ROUGE score of our model exceeds the score of the stateoftheart model in the original NLPCC2017 Shared Task 3, where a sentence summary is generated from each Chinese news article.

A Framework for Word Embedding Based Automatic Text Summarization and Evaluation Text summarization is a process of producing a concise version of text (summary) from one or more information sources. If the generated summary preserves meaning of the original text, it will help the users to make fast and effective decision. However, how much meaning of the source text can be preserved is becoming harder to evaluate. The most commonly used automatic evaluation metrics like RecallOriented Understudy for Gisting Evaluation (ROUGE) strictly rely on the overlapping ngram units between reference and candidate summaries, which are not suitable to measure the quality of abstractive summaries. Another major challenge to evaluate text summarization systems is lack of consistent ideal reference summaries. Studies show that human summarizers can produce variable reference summaries of the same source that can significantly affect automatic evaluation metrics scores of summarization systems. Humans are biased to certain situation while producing summary, even the same person perhaps produces substantially different summaries of the same source at different time. This paper proposes a word embedding based automatic text summarization and evaluation framework, which can successfully determine salient topn sentences of a source text as a reference summary, and evaluate the quality of systems summaries against it. Extensive experimental results demonstrate that the proposed framework is effective and able to outperform several baseline methods with regard to both text summarization systems and automatic evaluation metrics when tested on a publicly available dataset.