Automatic text summarization is an active investigation region determined as removing snippets or introductory sentences of a massive document and relating them short form documents. Text Summarization can be either costefficient time-efficient. An abstractive extractive summary was studied with distinct algorithms comprising deep learning (DL), graph, statistical-based techniques. DL has atta...

This paper proposes an improved approach of summarization for spoken multi-party interaction, in which a multi-layer graph with hidden parameters is constructed. The graph includes utterance-to-utterance relation, utterance-to-parameter weight, and speaker-to-parameter weight. Each utterance and each speaker are represented as a node in the utterance-layer and speaker-layer of the graph respect...

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of...

With vast amounts of text being available in electronic format, such as news and social media, automatic multi-document summarization can help extract the most important information. We present and evaluate a novel method for automatic extractive multi-document summarization. The method is purely combinatorial, based on bicliques in the bipartite word-sentence occurrence graph. It is particular...

More and more information is available recently. To find a chance i.e., an important event for decision-making, we have to be prepared for the chance. Recent progress of automatic summarization may contribute to Chance Discovery in that it helps a user read a lot of documents easily and be prepared for the chance. In this paper, we develop a new method for multi-document summarization which ext...

We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query, we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the summarization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs rather than single words. Our experiments w...

This interactive presentation describes LexNet, a graphical environment for graph-based NLP developed at the University of Michigan. LexNet includes LexRank (for text summarization), biased LexRank (for passage retrieval), and TUMBL (for binary classification). All tools in the collection are based on random walks on lexical graphs, that is graphs where different NLP objects (e.g., sentences or...

This paper describes a summarization system that aims to provide a set of languageindependent and generic methods for generating extractive summaries. The proposed methods are realized as operators to a generic character n-gram graph representation of texts, towards the selection of content and removal of redundancy. This work defines the set of generic operators upon n-gram graphs and proposes...

Single-document summarization and multidocument summarization are very closely related tasks and they have been widely investigated independently. This paper examines the mutual influences between the two tasks and proposes a novel unified approach to simultaneous single-document and multidocument summarizations. The mutual influences between the two tasks are incorporated into a graph model an...