In this paper, we formulate extractive summarization as a risk minimization problem and propose a unified probabilistic framework that naturally combines supervised and unsupervised summarization models to inherit their individual merits as well as to overcome their inherent limitations. In addition, the introduction of various loss functions also provides the summarization framework with a fle...

Existing summarization approaches for XML concentrate on extracting common structure and compressing the data, to optimize storage and speed up queries. Neither compression, nor structure extraction suffices for advanced, content-based summarization tasks. We present a set of tools for semi-automatic summarization of XML collections, where the user can specify semantically relevant features for...

© Document summarization using Wikipedia Krishnan Ramanathan, Yogesh Sankarasubramaniam, Nidhi Mathur, Ajay Gupta HP Laboratories HPL-2009-39 Single Document Summarization, Wikipedia, ROUGE Although most of the developing world is likely to first access the Internet through mobile phones, mobile devices are constrained by screen space, bandwidth and limited attention span. Single document summa...

Text summarization is a process of distilling the most important content from text documents. While human beings have proven to be extremely capable summarizers, computer based automatic abstracting and summarizing has proven to be extremely challenging tasks. In this paper we report our experience with applying extractive summarization techniques to process news articles, economic reports and ...

In this paper, we describe a sentence position based summarizer that is built based on a sentence position policy, created from the evaluation testbed of recent summarization tasks at Document Understanding Conferences (DUC). We show that the summarizer thus built is able to outperform most systems participating in task focused summarization evaluations at Text Analysis Conferences (TAC) 2008. ...

With the proliferation of online information, text summarization has become essential to provide enhanced mechanisms to perceive and present effective textual information. It is very difficult for human beings to manually summarize large documents of text. Automatic text summarization has become an important and timely tool for assisting and interpreting text information. Text summarization is ...

Abstractive summarization has been a longstanding and long-term goal in automatic summarization, because systems that can generate abstracts demonstrate a deeper understanding of language and the meaning of documents than systems that merely extract sentences from those documents. Genest (2009) showed that summaries from the top automatic summarizers are judged as comparable to manual extractiv...

Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, graph processing modern hardware. However, bulk literature on surprisingly overlooks possibility having edges different types. In this paper, we study novel problem producing summaries multi-relation...

This paper presents a survey of recent extractive query-based summarization techniques. We explore approaches for single document and multidocument summarization. Knowledge-based and machine learning methods for choosing the most relevant sentences from documents with respect to a given query are considered. Further, we expose tailored summarization techniques for particular domains like medica...

Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstractive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the original documents. Abstractive summarization...