Applying natural language generation to indicative summarization

The task of creating indicative summaries that help a searcher decide whether to read a particular document is a difficult task. This paper examines the indicative summarization task from a generation perspective, by first analyzing its required content via published guidelines and corpus analysis. We show how these summaries can be factored into a set of document features, and how an implemented content planner uses the topicality document feature to create indicative multidocument query-based summaries

A Review On Automatic Text Summarization Approaches

It has been more than 50 years since the initial investigation on automatic text summarization was started.Various techniques have been successfully used to extract the important contents from text document to represent document summary.In this study,we review some of the studies that have been conducted in this still-developing research area.It covers the basics of text summarization,the types of summarization,the methods that have been used and some areas in which text summarization has been applied.Furthermore,this paper also reviews the significant efforts which have been put in studies concerning sentence extraction,domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it.In addition,the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study

Emotional Feedback Generation for Physical Therapy

Consider a personal assistant who lives with physical therapy patients, reminding them to perform various exercises and giving patients feedback about how well they have enacted such exercises. The assistant has learned, through the patient\u27s interactions with his or her physical therapist, to personalize its commands and responses to the patient. Having such an assistant would decrease the time it takes for patients to recover via a more regimented exercise routine and would give physical therapists a much more detailed account of the activities of their patients. Here I demonstrate a system that, given an encoding of a patient\u27s exercise, generates emotionally-manipulated sentence feedback about how to better perform the exercise. In essence, the system is composed of two primary modules: a reasoning module and a natural language generation module. For my results, I present a technique to generating instances of emotionally-altered exercise feedback on a stern-nurturing axis. I show a method for relating feedback words to deficiencies in exercise performance. Finally, I demonstrate a proof of concept technique for generating feedback given real-time skeleton mapping software. In future work, the system will be integrated into a physical therapy social avatar, which will interact as an assistant to patients within their home

A graph-based approach towards automatic text summarization

Due to an exponential increase in number of electronic documents and easy access to information on the Internet, the need for text summarization has become obvious. An ideal summary contains important parts of the original document, eliminates redundant information and can be generated from single or multiple documents. There are several online text summarizers but they have limited accessibility and generate somewhat incoherent summaries. We have proposed a Graph-based Automatic Summarizer (GAUTOSUMM), which consists of a pre-processing module, control features and a post-processing module. For evaluation, two datasets, Opinosis and DUC 2007 are used and generated summaries are evaluated using ROUGE metrics. The results show that GAUTOSUMM outperforms the online text summarizers in eight out of ten topics both in terms of the summary quality and time performance. A user interface has also been built to collect the original text and the desired number of sentences in the summary.text summarizationgraph-basedautomatic text summarizationGAUTOSUM

Applying natural language generation to indicative summarization

The task of creating indicative summaries that help a searcher decide whether to read a particular document is a difficult task. This paper examines the indicative summarization task from a generation perspective, by first analyzing its required content via published guidelines and corpus analysis. We show how these summaries can be factored into a set of document features, and how an implemented content planner uses the topicality document feature to create indicative multidocument query-based summaries.