Topic
Multi-document summarization
About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.
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30 Apr 2000
TL;DR: An improved task-based evaluation method of summarization is developed, the accuracy of which is increased by specifying the details of the task including background stories, and by assigning ten subjects per summary sample.
Abstract: We have developed an improved task-based evaluation method of summarization, the accuracy of which is increased by specifying the details of the task including background stories, and by assigning ten subjects per summary sample. The method also serves precision/recall pairs for a variety of situations by introducing multiple levels of relevance assessment. The method is applied to prove phrase-represented summary is most effective to select relevant documents from information retrieval results.
11 citations
01 Dec 2015
TL;DR: This paper proposes a new technique, namely window-based sentence representation (WSR), to obtain the features of sentences using pre-trained word vectors, developed based on the Extreme Learning Machine (ELM).
Abstract: Multi-document summarization has gained popularity in many real world applications because significant information can be obtained within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences, whose performance relies heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features for sentence representation. In this paper, we leverage on word embedding to represent sentences so as to avoid the intensive labor of feature engineering. We propose a new technique, namely window-based sentence representation (WSR), to obtain the features of sentences using pre-trained word vectors. The method is developed based on the Extreme Learning Machine (ELM). Our proposed framework does not require any prior knowledge and therefore can be applied to various document summarization tasks with different languages, written styles and so on. We evaluate our proposed method on the DUC 2006 and 2007 datasets. This proposed method achieves superior performance compared with state-of-the-arts document summarization algorithms with a much faster learning speed.
11 citations
01 Aug 2015
TL;DR: Singular Value Decomposition (SVD) is used to generate the summary because it finds the dimensions of the sentence vectors which are principal and mutually orthogonal and guaranty the relevance to original text document and non-redundancy respectively in machine generated summary.
Abstract: Text Summarization is a method of reducing the original text document into a short description This short version retains the meaning and information content of the original text document It is a difficult task for human beings to generate the summary for very large documents manually The linguistic and statistical features of sentence can be used to find the importance of sentences The Latent Semantic Analysis (LSA) captures automatically the semantic relationships between the sentences as a human being thinks In this paper Singular Value Decomposition (SVD) is used to generate the summary SVD finds the dimensions of the sentence vectors which are principal and mutually orthogonal These properties guaranty the relevance to original text document and non-redundancy respectively in machine generated summary
11 citations
28 Jun 2011
TL;DR: It is concluded that the use of COMPENDIUM is appropriate for producing summaries of research papers automatically, going beyond the simple selection of sentences.
Abstract: This paper presents COMPENDIUM, a text summarization system, which has achieved good results in extractive summarization. Therefore, our main goal in this research is to extend it, suggesting a new approach for generating abstractive-oriented summaries of research papers. We conduct a preliminary analysis where we compare the extractive version of COMPENDIUM (COMPENDIUME) with the new abstractiveoriented approach (COMPENDIUME-A). The final summaries are evaluated according to three criteria (content, topic, and user satisfaction) and, from the results obtained, we can conclude that the use of COMPENDIUM is appropriate for producing summaries of research papers automatically, going beyond the simple selection of sentences.
11 citations
TL;DR: This paper uses complex network concepts to devise an extractive Multi Document Summarization (MDS) method, which extracts the most central sentences from several textual sources, where texts are represented as networks, where nodes represent sentences and the edges are established based on the number of shared words.
Abstract: Due to the large amount of textual information available on Internet, it is of paramount relevance to use techniques that find relevant and concise content. A typical task devoted to the identification of informative sentences in documents is the so called extractive document summarization task. In this paper, we use complex network concepts to devise an extractive Multi Document Summarization (MDS) method, which extracts the most central sentences from several textual sources. In the proposed model, texts are represented as networks, where nodes represent sentences and the edges are established based on the number of shared words. Differently from previous works, the identification of relevant terms is guided by the characterization of nodes via dynamical measurements of complex networks, including symmetry, accessibility and absorption time. The evaluation of the proposed system revealed that excellent results were obtained with particular dynamical measurements, including those based on the exploration of networks via random walks.
11 citations