In paper, we propose an unsupervised text summarization model which generates a summary by extracting salient sentences in given document(s). In particular, we model text summarization as an integer linear programming problem. One of the advantages of this model is that it can directly discover ...
In the experiments, we use the Large-scale Chinese Short Text Summarization Dataset (LCSTS) to evaluate the model, and the ROUGE index was used to evaluate the results. The experimental results show that the proposed model is effective and feasible for abstractive text summarization....
DiscoBERT:A Discourse-Aware Neural Extractive Model for Text Summarization,ACL2020,论文链接 4.1 论文整体思路 1.论文的模型主体是BertSum,作者认为直接抽取原文句子然后组合成摘要的方法存在很多问题,如内容存在大量冗余信息、前后语句不通顺等,所以作者就想了一个折中的方法,整体上依然采用抽取式,但并不是直接抽取...
Here, you create an instance of the Summarizer class named bert_model. This instance is now ready to perform the summarization task using the BERT model, simplifying the complex processes of embedding sentences and clustering into an accessible interface. Generate and print a summary. summary = be...
Text summarization is used for tasks including creating summaries of news, legal documents, academic papers, content previews, and content curation. The ambiguity, coherence, bias, and fluency of the text used to train the model as well as information lo
——A Neural Attention Model for Abstractive Sentence Summarization 1.问题定义: 我们的目标是输入一个句子x,然后输出一个更短的句子y作为x的摘要。因此问题定义如下: 其中Y定义为长度为N的所有可能的句子,当我们获得的摘要y是来自集合Y,那么我们认为这个摘要系统是abstractive的。
MYTextSum: A Malay Text Summarizer Model Using a Constrained Pattern-Growth Sentence Compression TechniqueText SummarizationSentence Compression Sequential Pattern-GrowthMalayAs more information becomes accessible online, users are faced with difficulties in digesting and selecting important information from ...
On the basis of the experimental results, we also augment the summarization model so that it takes into account the relevance to the document cluster. Through experiments, we showed that the augmented model is at least comparable to the best-performing method of DUC'04. 展开 ...
[5] SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization:https://arxiv...
1. Extractive Summarization:根据词语重要性、句子重要性排序,抽取出重要度高的句子,从而形成摘要。主要是对文本的选择,算法过程相对更容易,但是对于复杂的文本时,很难仅仅通过选择文本来形成摘要,如小说。 2. Abstractive Summarization:过程更为复杂,但生成能力更强,可认为有一定的概括能力。