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.

A supervised learning method combine with dimensionality reduction in Vietnamese text summarization

Abstract: The World Wide Web has brought us a vast amount of online information. When we search with a keyword, data feedback from many different websites and the user cannot read all the information. So that, text summarization has become a hot topic, it has attracted experts in data mining and natural language processing field. For Vietnamese, some methods of text summarization based on that have been proposed for English also bring some significant results. However, still remain some difficult problems to treat with the Vietnamese language processing, typical in this is the Vietnamese text segmentation tool and text summarization corpus. In this paper, we present a Vietnamese text summarization method based on sentence extraction approach using neural network for learning combine reducing dimensional features to overcome the cost when building term sets and reduce the computational complexity. The experimental results show that our method is really effective in reducing computational complexity, and is better than some methods that have been proposed previous.

Multi-document Hyperedge-based Ranking for Text Summarization

Abstract: In a multi-document settings, graph-based extractive summarization approaches build a similarity graph out of sentences in each cluster of documents then use graph centrality approaches to measure the importance of sentences The similarity is computed between each pair of sentences However, it is not clear if such approach captures high-order relations among more than two sentences or can differentiate between descriptive sentences of the cluster in comparison with other clusters In this paper, we propose to model sentences as hyperedges and words as vertices using a hypergraph and combine it with topic signatures to differentiate between descriptive sentences and non-descriptive sentences To rank sentences, we propose a new random walk over hyperedges that will prefer descriptive sentences of the cluster when measuring their centrality scores Our approach outperform a number of baseline in the DUC 2001 dataset using the ROUGE metric

A domain-specific automatic text summarization using fuzzy logic

Abstract: The amount of information on World Wide Web has increased enormously. In this context there is a need for text summarization. It creates summaries of the documents that consist of important sentences in the document. The summaries help the readers to make decision as to read the whole document or not thus acting as a time saver. Various Techniques have been proposed for text summarization by researchers that can be broadly classified into two types: Extraction and

Multi-Document Summarization Using TF-IDF Algorithm.

Abstract: With the increase in amout of data and information one has to deal with,now a days,going through all the documents is a time consuming process.We are implementing an android application that helps organizations such as law firms to manage the hundreds of documents and to get summary of these documents.We are also using concept of ontology for this application.Ontology is basically the relationship between entities.The application that we are implementing allow the users to search for files in the database,upload files and summarize multiple documents.

Multi-modal and multi-scale photo collection summarization

Abstract: With the proliferation of digital cameras and mobile devices, people are taking much more photos than ever before However, these photos can be redundant in content and varied in quality Therefore there is a growing need for tools to manage the photo collections One efficient photo management way is photo collection summarization which segments the photo collection into different events and then selects a set of representative and high quality photos (key photos) from those events However, existing photo collection summarization methods mainly consider the low-level features for photo representation only, such as color, texture, etc, while ignore many other useful features, for example high-level semantic feature and location Moreover, they often return fixed summarization results which provide little flexibility In this paper, we propose a multi-modal and multi-scale photo collection summarization method by leveraging multi-modal features, including time, location and high-level semantic features We first use Gaussian mixture model to segment photo collection into events With images represented by those multi-modal features, our event segmentation algorithm can generate better performance since the multi-modal features can better capture the inhomogeneous structure of events Next we propose a novel key photo ranking and selection algorithm to select representative and high quality photos from the events for summarization Our key photo ranking algorithm takes the importance of both events and photos into consideration Furthermore, our photo summarization method allows users to control the scale of event segmentation and number of key photos selected We evaluate our method by extensive experiments on four photo collections Experimental results demonstrate that our method achieves better performance than previous photo collection summarization methods