Extractive summarization techniques cannot generate document summaries shorter than a single sentence, something that is often required. An ideal summarization system would understand each document and generate an appropriate summary directly from the results of that understanding. A more practical approach to this problem results in the use of an approximation: viewing summarization as a probl...

In the field of multi-document summarization, the Pyramid method has become an important approach for evaluating machine-generated summaries. The method is based on the manual annotation of text spans with the same meaning in a set of human model summaries. In this paper, we present an unsupervised, probabilistic topic modeling approach for automatically identifying such semantically similar te...

Over the last few years there has been substantial research on text summarization, but comparatively little research has been carried out on adaptable components that allow rapid development and evaluation of summarization solutions. This paper presents a set of adaptable summarization components together with well-established evaluation tools, all within the GATE paradigm. The toolkit includes...

Problem statement: Text summarization can be of different nature ranging from indicative summary that identifies the topics of the document to informative summary which is meant to represent the concise description of the original document, providing an idea of what the whole content of document is all about. Approach: Single document summary seems to capture both the information well but it ha...

For the blessing of World Wide Web, the corpus of online information is gigantic in its volume. Search engines have been developed such as Google, AltaVista, Yahoo, etc., to retrieve specific information from this huge amount of data. But the outcome of search engine is unable to provide expected result as the quantity of information is increasing enormously day by day and the findings are abun...

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...

Abstract The Query-Focused Text Summarization (QFTS) task aims at building systems that generate the summary of text document(s) based on given query. A key challenge in addressing this is lack large labeled data for training summarization model. In article, we address by exploring a series domain adaptation techniques. Given recent success pre-trained transformer models wide range natural lang...

In this paper, we evaluate our automatic text summarization system in multilingual context. We participated in both single document and multi-document summarization tasks of MultiLing 2015 workshop. Our method involves clustering the document sentences into topics using a fuzzy clustering algorithm. Then each sentence is scored according to how well it covers the various topics. This is done us...