Summarization is an important challange in natural language processing. Deep learning methods, however, have not been widely used in text summarization, although neural networks have been proved to be powerful in natural language processing. In this paper, an encoder-decoder neural network model is applied to text summarization, as an important step toward this task. Besides, a hierarchical mod...

Multi-document summarization aims at delivering the majority of information content from multiple documents using much less lengthy texts, usually a short paragraph of several hundred words. This paper surveys several different approaches to multi-document summarization by first building a unified high level view of the multi-document summarization problem, and then comparing different approach...

We participated in three multi-document summarization tasks at the DUC-2003 formal run and evaluated the performance of our summarization system. Our summarization system based on sentence extraction also incorporated a module to estimate similarity between sentences for multi-document summarization. The similarity information was used for selecting the representative sentence among similar sen...

We propose to automatically summarize student responses to reflection prompts and introduce a novel summarization algorithm that differs from traditional methods in several ways. First, since the linguistic units of student inputs range from single words to multiple sentences, our summaries are created from extracted phrases rather than from sentences. Second, the phrase summarization algorithm...

Multi-document summarization is the automatic production of a unique summary from a collection of texts. This task has become very important, since it assists the information processing in days where the amount of information is growing considerably. In this paper, we propose a statistical generative approach for multi-document summarization. In particular, we formulate the multi-document summa...

Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinitypreserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint ...

Video summarization offers an intelligent way of reducing the video content temporally to satisfy the user preference for a shorter viewing time, and/or to meet the bit budget limitations in storage/communications. In this paper we formulate the summarization problem as a rate-distortion optimization problem under bit budget and frame skip constraints, and introduce a summarization distortion m...

In recent times, data is growing rapidly in every domain such as news, social media, banking, education, etc. Due to the excessiveness of data, there is a need of automatic summarizer which will be capable to summarize the data especially textual data in original document without losing any critical purposes. Text summarization is emerged as an important research area in recent past. In this re...

Due to increasing use of internet and online technologies or online data, there is vast increase in the electronic documents. When a data is being retrieved from such a huge collection of electronic documents, hundreds and thousands of documents are retrieved. Hence, for user, it is not possible to read all the retrieved documents. Also, these documents contain redundant information. In such si...

The goal of automated summarization is to tackle the “information overload” problem by extracting and perhaps compressing the most important content of a document. Due to the difficulty that singledocument summarization has in beating a standard baseline, especially for news articles, most efforts are currently focused on multi-document summarization. The goal of this study is to reconsider the...