Multi-document summarization is the automatic production of a unique summary from a collection of texts. In this paper, we propose a statistical generative approach for multi-document summarization that combines simple information such as sentence position in the text and semantic-discursive information from CST (Cross-Document Structure Theory). In particular, we formulate the multi-document s...

Abstract. The increase of information available in the form of text, led to the need of extensive research in the area of text summarization. Early the researches in this area started with single document summarization and drove towards multi document summarization. We present here a comparative review of the recent progress in the field of multi document summarization. The strengths and weakne...

We study a new content-based method for the evaluation of text summarization systems without human models which is used to produce system rankings. The research is carried out using a new content-based evaluation framework called FRESA to compute a variety of divergences among probability distributions. We apply our comparison framework to various well-established content-based evaluation measu...

In this paper we, present (1) an unsupervised system for AESOP task and (2) a generic multi-document summarization system for guided summarization task. We propose the use of: (1) the role and importance of words and sentences in document and, (2) number and coverage strength of topics in document for both AESOP and Guided summarization task. We also use some other statistical features, simple ...

Summarization, an extremely important technique in Data Mining is an automatic learning technique aimed to extract the most valuable information from a large size document or the articles. The goal is to create the summary of the document, but substantially different from each other. Text Document summarization refers to the summarization of text documents based upon their content. The proposed...

Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. Recent works have been devoted leveraging text summarization achieved promising results. However, these summarization-based methods did not take full advantage of the summary including ignoring inherent interactions between document. As a result, they limited representat...

With the rapid growth of internet also there is increase in on-line text document. Accessing such huge number of electronic textual documents creates challenge in front of user. It requires user to analyze the searched results one by one until satisfied information is acquired, which is time consuming process. Summary of document helps user to know about the page is about what? There are differ...

Query-based document summarization aims to extract or generate a summary of which directly answers is relevant the search query. It an important technique that can be beneficial variety applications such as engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based are short in numbers existing also limited both scale quality. Moreover, bes...

We show that by making use of information common to document sets belonging to a common category, we can improve the quality of automatically extracted content in multi-document summaries. This simple property is widely applicable in multi-document summarization tasks, and can be encapsulated by the concept of category-specific importance (CSI). Our experiments show that CSI is a valuable metri...