As information is available in abundance for every topic on internet, condensing the
important information in the form of summary would benefit a number of users. Hence, there …

Topic modeling, or identifying the set of topics that occur in a collection of articles, is one of
the primary objectives of text mining. One of the big challenges in topic modeling is …

We present a novel unsupervised query-focused multi-document summarization approach.
To this end, we generate a summary by extracting a subset of sentences using the Cross …

Multi-document Summarization (MDS) is of great value to many real world applications.
Many scoring models are proposed to select appropriate sentences from documents to form …

Extractive multi-document summarization systems usually rank sentences in a document set
with some ranking strategy and then select a few highly ranked sentences into the summary …

Multi-document summarization is used to extract the main ideas of the documents and put
them into a short summary. In multi-document summarization, it is important to reduce …

摘要利用专利文献数据识别技术领域的技术主题演化发展路径并分析其发展趋势, 对于科技界,
企业界进行专利技术创新具有重要的意义. 首先, 使用Open IE 5.1 进行SAO (subject-action …

In this paper, we formulate a sparse optimization framework for extractive document
summarization. The proposed framework has a decomposable convex objective function …

This manuscript provides a comprehensive exploration of Probabilistic latent semantic
analysis (PLSA), highlighting its strengths, drawbacks, and challenges. The PLSA, originally …

Non-negative Matrix Factorization (NMF) models the topics of a text corpus by decomposing
the matrix of term frequency-inverse document frequency (TF-IDF) representation, X, into two …