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 1 develops a method for combining queryrelevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in reranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results indicate some benefits for MMR dive...

We present our state of the art multilingual text summarizer capable of single as well as multi-document text summarization. The algorithm is based on repeated application of TextRank on a sentence similarity graph, a bag of words model for sentence similarity and a number of linguistic preand post-processing steps using standard NLP tools. We submitted this algorithm for two different tasks of...

Abstract The availability of large-scale datasets has driven the development neural models that create generic summaries for single or multiple documents. For query-focused summarization (QFS), labeled training data in form queries, documents, and is not readily available. We provide a unified modeling framework any kind summarization, under assumption all are response to query, which observed ...

“The process of multi-document summarization is producing a single summary of a collection of related documents. In this work we focus on generic extractive Arabic multi-document summarizers. We also describe the cluster approach for multi-document summarization. The problem with multi-document text summarization is redundancy of sentences, and thus, redundancy must be eliminated to ensure cohe...

The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarizatio...

Although recent years has seen increased and successful research efforts in the areas of single -document summarization, multi-document summarization, and information extraction, very few investigations have explored the potential of merging summarization and information extraction techniques. This paper presents and evaluates the initial version of RIPTIDES, a system that combines information ...

Since 2001, the Document Understanding Conferences have been the forum for researchers in automatic text summarization to compare methods and results on common test sets. Over the years, several types of summarization tasks have been addressed—single document summarization, multi-document summarization, summarization focused by question, and headline generation. This paper is an overview of the...

Traditional approaches to extractive summarization rely heavily on humanengineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor. This architecture allows us to develop different classe...

Document summarization deals with providing condensed version of the original document. We present an extractive informative single medical document summarization approach. We compare the tokens in the sentence with cue words. A sentence ranking method is used to extract the important sentences. The existing summarizers are used for performance analysis.