We design a class of submodular functions meant for document summarization tasks. These functions each combine two terms, one which encourages the summary to be representative of the corpus, and the other which positively rewards diversity. Critically, our functions are monotone nondecreasing and submodular, which means that an efficient scalable greedy optimization scheme has a constant factor...

We describe iNeATS – an interactive multi-document summarization system that integrates a state-of-the-art summarization engine with an advanced user interface. Three main goals of the system are: (1) provide a user with control over the summarization process, (2) support exploration of the document set with the summary as the staring point, and (3) combine text summaries with alternative prese...

In this paper, we propose a novel context inference-based approach for sentences ordering in mult i-document summarization application. Our method first detects whether or not two summarizat ion sentences should be adjacent according to the similarity between one summarizat ion sentence and the context of the other one, and then it computes the reliability of the adjacent summarization sentence...

In this paper we consider a method for extraction of sets of semantically similar language expressions representing different participants of the text story – thematic chains. The method is based on the structural organization of news clusters and exploits comparison of various contexts of words. The word contexts are used as a basis for extracting multiword expressions and constructing themati...

In this paper we consider a method for extraction of sets of semantically similar language expressions representing different participants of the text story – thematic nodes. The method is based on the structural organization of news clusters and exploits comparison of various contexts of words. The word contexts are used as a basis for multiword expression extraction and thematic node construc...

In this paper we study the effectiveness of applying sentence compression on an extraction based multi-document summarization system. Our results show that pure syntactic-based compression does not improve system performance. Topic signature-based reranking of compressed sentences does not help much either. However reranking using an oracle showed a significant improvement remains possible.

This paper discusses a text extraction approach to multidocument summarization that builds on single-document summarization methods by using additional, available in-, formation about the document set as a whole and the relationships between the documents. Multi-document summarization differs from single in that the issues of compression, speed, redundancy and passage selection are critical in ...

This paper examines the summarization of events that evolve through time. It discusses di erent types of evolution taking into account the time in which the incidents of an event are happening and the different sources reporting on the speci c event. It proposes an approach for multi-document summarization which employs \messages" for representing the incidents of an event and cross-document re...

Although there has been much theoretical work on using various information status distinctions to explain the form of references in written text, there have been few studies that attempt to automatically learn these distinctions for generating references in the context of computer-regenerated text. In this article, we present a model for generating references to people in news summaries that in...

Existing work for abstractive multidocument summarization utilise existing phrase structures directly extracted from input documents to generate summary sentences. These methods can suffer from lack of consistence and coherence in merging phrases. We introduce a novel approach for abstractive multidocument summarization through partial dependency tree extraction, recombination and linearization...