In this paper, we introduce the Priberam Compressive Summarization Corpus, a new multi-document summarization corpus for European Portuguese. The corpus follows the format of the summarization corpora for English in recent DUC and TAC conferences. It contains 80 manually chosen topics referring to events occurred between 2010 and 2013. Each topic contains 10 news stories from major Portuguese n...

For multi-document summarization where documents are collected over an extended period of time, the subject in a document changes over time. This paper focuses on subject shift and presents a method for extracting key paragraphs from documents that discuss the same event. Our extraction method uses the results of event tracking which starts from a few sample documents and finds all subsequent d...

In this paper, we present MDSWriter, a novel open-source annotation tool for creating multi-document summarization corpora. A major innovation of our tool is that we divide the complex summarization task into multiple steps which enables us to efficiently guide the annotators, to store all their intermediate results, and to record user–system interaction data. This allows for evaluating the ind...

We describe our work on the development of Language and Evaluation Resources for the evaluation of summaries in English and Chinese. The language resources include a parallel corpus of English and Chinese texts which are translations of each other, a set of queries in both languages, clusters of documents relevants to each query, sentence relevance measures for each sentence in the document clu...

This paper presents two corpora produced within the RPM2 project: a multi-document summarization corpus and a sentence compression corpus. Both corpora are in French. The first one is the only one we know in this language. It contains 20 topics with 20 documents each. A first set of 10 documents per topic is summarized and then the second set is used to produce an update summarization (new info...

Since most of news articles report several events and these events are referred in many related documents, we propose an event-based approach to visualize documents as graph on different conceptual granularities. With graphbased ranking algorithm, we illustrate the application of document graph to multi-document summarization. Experiments on DUC data indicate that our approach is competitive wi...

For the blessing of World Wide Web, the corpus of online information is gigantic in its volume. Search engines have been developed such as Google, AltaVista, Yahoo, etc., to retrieve specific information from this huge amount of data. But the outcome of search engine is unable to provide expected result as the quantity of information is increasing enormously day by day and the findings are abun...

The amount of data available in the electronic environment is increasing day by with development technology. It becomes tough and time consuming for users to access information they desire within this data. Automatic text summarization systems have been developed reach desired texts a shorter than that manual summarization. In paper, new extractive model proposed. proposed model, inclusion sent...

Due to the rapid development of internet technology, social media and popular research article databases have generated many open text information. This large amount textual information leads 'Big Data'. Textual can be recorded repeatedly about an event or topic on different websites. Text summarization (TS) is emerging field that helps produce summary from a single multiple documents. The redu...

Automatic text summarization, the reduction of a text to its essential content is fundamental for an on-line information society. Although many summarization algorithms exist, there are few tools or infrastructures providing capabilities for developing summarization applications. This paper presents a new version of SUMMA, a text summarization toolkit for the development of adaptive summarizati...