This paper presents a novel approach for legal document summarization. Proposed is based on Ripple-Down Rules (RDR). It an incremental knowledge acquisition method. RDR allows us to quickly build extendable base using classification rules. The rules are written set of features. Summary generated the identified rhetorical roles in document. Experiments demonstrate that Legal Document summarizati...

This paper will propose a personal news secretariat that helps on-line readers absorb news information from multiple sources. Such a news secretariat eliminates the redundant information in the news, and reorganizes the news for readers. This multiple document summarization employs named entities and other signatures to cluster news stream; employs punctuation marks, linking elements, and topic...

With the exponential growth of textual information on web and in multimedia, query-focused multi-document summarization (QFMS) has emerged as a critical research area. QFMS aims to generate concise summaries that address user queries satisfy their needs. This paper provides comprehensive survey state-of-the-art approaches QFMS, focusing specifically graph-based clustering-based methods. Each ap...

Automatic text summarization aims to cut down readers’ time and cognitive effort by reducing the content of a document without compromising on its essence. Ergo, informativeness is prime attribute summary generated an algorithm, selecting sentences that capture essence primary goal extractive summarization. In this paper, we employ Shannon’s entropy sentences. We Non-negative Matrix Factorizati...

Automatic summarization [5] can be defined as the procedure to create a short version of a text by a computer program. Its product still contains the most important points of the existing text. Multi-document summarization [6] can be defined as an automatic procedure which extracts information from multiple texts that is written about the same topic. Resulting summary report allows individual u...

In this paper, we introduce a large-scale test collection for multiple document summarization, the Text Summarization Challenge 3 (TSC3) corpus. We detail the corpus construction and evaluation measures. The significant feature of the corpus is that it annotates not only the important sentences in a document set, but also those among them that have the same content. Moreover, we define new eval...

Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other ...