A New Approach to Automatic Summarization by Using Latent Dirichlet Allocation in Conditional Random Field Xiaofeng Wu, Chengqing Zong (National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, China) Abustract: In recent years, Latent Dirichlet Allocation(LDA) has been used more and more in Document Clustering, Classification, Segmentation, and some one has used it in ...

Abstract The availability of large-scale datasets has driven the development neural models that create generic summaries for single or multiple documents. For query-focused summarization (QFS), labeled training data in form queries, documents, and is not readily available. We provide a unified modeling framework any kind summarization, under assumption all are response to query, which observed ...

The ability to separate opinion from fact within text requires accurate detection of subjective language. Most work in this area has relied on some level of human supervision in the form of hand-tagging or word-list construction. Drawing from the unique nature of the multi-document summarization task, we present an unsupervised method for discovering subjectivity clues using freely available mu...

While most people have a clear idea of what a single document summary should look like, this is not immediately obvious for a multidocument summary. There are many new questions to answer concerning the amount of documents to be summarized, the type of documents, the kind of summary that should be generated, the way the summary gets presented to the user, etc. The many approaches possible to mu...

In this study, we analyzed the effects of applying different levels of stemming approaches such as fixed-length word truncation and morphological analysis for multi-document summarization (MDS) on Turkish, which is an agglutinative and morphologically rich language. We constructed a manually annotated MDS data set, and to our best knowledge, reported the first results on Turkish MDS. Our result...

The update task of multi-document summarization aims at automatically generating the summaries of some event developing with time going. Based on our previous system of multi-document summarization, we summarize the document sets with or without history. In our previous system, the design of features is the important part. Here, in order to adapt to the updating task we introduce a new ‘filteri...

The problem of extractive text summarization for a collection of documents is defined as selecting a small subset of sentences so the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a new model for the problem of extractive summarization, where we strive to obtain a summary that preserves the information coverage as much as poss...