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...

We model document summarization as a nonlinear 0-1 programming problem where an objective function is defined as Heronian mean of the objective functions enforcing the coverage and diversity. The proposed model implemented on a multi-document summarization task. Experiments on DUC2001 and DUC2002 datasets showed that the proposed model outperforms the other summarization methods. Index Terms – ...

We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of documentsummary pairs. When used for singledocument summarization, our approach, combined with the “beginning of document” heuristic, outperforms a state-ofthe-art summarizer and the beginning-ofarticle baseline in both automatic and manual evaluations. These results represent an impo...

The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets. We present two QMDS datasets, which we construct using data augmentation methods: (1) transferring commonly used single-document CNN/Daily Mail summarization dataset to create QMDSCNN dataset, and (2) mining search-query logs QMDSIR dataset. The...

Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion...

We present a multi-document summarizer, MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We describe two new techniques, a centroid-based summarizer, and an evaluation scheme based on sentence utility and subsumption. We have applied this evaluation to both single and multiple document summaries. Finally, we describe two user studies tha...

Many methods of text summarization combining sentence selection and sentence compression have recently been proposed. Although the dependency between words has been used in most of these methods, the dependency between sentences, i.e., rhetorical structures, has not been exploited in such joint methods. We used both dependency between words and dependency between sentences by constructing a nes...

We present a discriminative model for single-document summarization that integrally combines compression and anaphoricity constraints. Our model selects textual units to include in the summary based on a rich set of sparse features whose weights are learned on a large corpus. We allow for the deletion of content within a sentence when that deletion is licensed by compression rules; in our frame...

The current state-of-the-art singledocument summarization method generates a summary by solving a Tree Knapsack Problem (TKP), which is the problem of finding the optimal rooted subtree of the dependency-based discourse tree (DEP-DT) of a document. We can obtain a gold DEP-DT by transforming a gold Rhetorical Structure Theory-based discourse tree (RST-DT). However, there is still a large differ...

Our Tipster Phase III research objective for the Summarization task is to produce a single summary across multiple documents returned from a search on an information retrieval system. An established set of metrics to evaluate the performance of our system is not available in this field at present, so this research is also developing a procedure to evaluate the summaries we create. We hope to un...