Rapid improvement of electronic documents in World Wide Web has made overload to the users in accessing the information. Therefore, abstracting the primary content from numerous documents related to same topic is highly essential. Summarization of multiple documents helps in valuable decision-making in less time. This paper proposed a framework named Adept Multi-Document Summarization (AMDS) fo...

Today, with rapid growth of the World Wide Web and creation of Internet sites and online text resources, text summarization issue is highly attended by various researchers. Extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. When, we are facing into large data volume documents, the extr...

Usually, presence of the same information in multiple documents is the main problem faced in effective information access. Instead of this redundant information thus accessed or retrieved, users are interested in retrieving information that addresses one or other several aspects. In such situation, text summarization proves to be very useful. Not only in Information retrieval, but it is an extr...

In this research work we have proposed two-tier architecture for document summarization. This architecture minimizes the redundancy and boosts the information relevancy in the summary by applying Probabilistic Latent Semantic Analysis (PLSA) at two levels. It also enhances the summarizer’s speed by using Incremental Expectation Maximization algorithm for PLSA learning rather than Expectation Ma...

PKUSUMSUM is a Java platform for multilingual document summarization, and it supports multiple languages, integrates 10 automatic summarization methods, and tackles three typical summarization tasks. The summarization platform has been released and users can easily use and update it. In this paper, we make a brief description of the characteristics, the summarization methods, and the evaluation...

Abstractive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-tosequence models. However, these models can still benefit from stronger natural language inference skills, since a correct summary is logically entailed by the input document, i.e., it should not contain any contradictory or unrelated informat...

We propose a multiple-document summarization system with user interaction that summarizes more than one document to a document. Our system extracts keywords from sets of documents to be summarized and shows k best keywords with respect to scoring by our system to a user on the screen. From the shown keywords, the user selects those reflecting the user's summarization need. Our system controls t...

Information Extraction (IE) and Summarization share the same goal of extracting and presenting the relevant information of a document. While IE was a primary element of early abstractive summarization systems, it's been left out in more recent extractive systems. However, extracting facts, recognizing entities and events should provide useful information to those systems and help resolve semant...

In this era, where electronic text information is exponentially growing and where time is a critical resource, it has become virtually impossible for any user to browse or read large numbers of individual documents. It is therefore important to explore methods of allowing users to locate and browse information quickly within collections of documents. Automatic text summarization of multiple doc...

This paper describes a novel clustering-based text summarization system that uses Multiple Sequence Alignment to improve the alignment of sentences within topic clusters. While most current clustering-based summarization systems base their summaries only on the common information contained in a collection of highly-related sentences, our system constructs more informative summaries that incorpo...