Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

CORWA: A Citation-Oriented Related Work Annotation Dataset

Academic research is an exploratory activity to discover new solutions to problems. By this nature, academic research works perform literature reviews to distinguish their novelties from prior work. In natural language processing, this literature review is usually conducted under the "Related Work" section. The task of related work generation aims to automatically generate the related work section given the rest of the research paper and a list of papers to cite. Prior work on this task has focused on the sentence as the basic unit of generation, neglecting the fact that related work sections consist of variable length text fragments derived from different information sources. As a first step toward a linguistically-motivated related work generation framework, we present a Citation Oriented Related Work Annotation (CORWA) dataset that labels different types of citation text fragments from different information sources. We train a strong baseline model that automatically tags the CORWA labels on massive unlabeled related work section texts. We further suggest a novel framework for human-in-the-loop, iterative, abstractive related work generation.Comment: Accepted by NAACL 202

Automatic bilingual text document summarization.

Lo Sau-Han Silvia.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 137-143).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Definition of a summary --- p.2Chapter 1.2 --- Definition of text summarization --- p.3Chapter 1.3 --- Previous work --- p.4Chapter 1.3.1 --- Extract-based text summarization --- p.5Chapter 1.3.2 --- Abstract-based text summarization --- p.8Chapter 1.3.3 --- Sophisticated text summarization --- p.9Chapter 1.4 --- Summarization evaluation methods --- p.10Chapter 1.4.1 --- Intrinsic evaluation --- p.10Chapter 1.4.2 --- Extrinsic evaluation --- p.11Chapter 1.4.3 --- The TIPSTER SUMMAC text summarization evaluation --- p.11Chapter 1.4.4 --- Text Summarization Challenge (TSC) --- p.13Chapter 1.5 --- Research contributions --- p.14Chapter 1.5.1 --- Text summarization based on thematic term approach --- p.14Chapter 1.5.2 --- Bilingual news summarization based on an event-driven approach --- p.15Chapter 1.6 --- Thesis organization --- p.16Chapter 2 --- Text Summarization based on a Thematic Term Approach --- p.17Chapter 2.1 --- System overview --- p.18Chapter 2.2 --- Document preprocessor --- p.20Chapter 2.2.1 --- English corpus --- p.20Chapter 2.2.2 --- English corpus preprocessor --- p.22Chapter 2.2.3 --- Chinese corpus --- p.23Chapter 2.2.4 --- Chinese corpus preprocessor --- p.24Chapter 2.3 --- Corpus thematic term extractor --- p.24Chapter 2.4 --- Article thematic term extractor --- p.26Chapter 2.5 --- Sentence score generator --- p.29Chapter 2.6 --- Chapter summary --- p.30Chapter 3 --- Evaluation for Summarization using the Thematic Term Ap- proach --- p.32Chapter 3.1 --- Content-based similarity measure --- p.33Chapter 3.2 --- Experiments using content-based similarity measure --- p.36Chapter 3.2.1 --- English corpus and parameter training --- p.36Chapter 3.2.2 --- Experimental results using content-based similarity mea- sure --- p.38Chapter 3.3 --- Average inverse rank (AIR) method --- p.59Chapter 3.4 --- Experiments using average inverse rank method --- p.60Chapter 3.4.1 --- Corpora and parameter training --- p.61Chapter 3.4.2 --- Experimental results using AIR method --- p.62Chapter 3.5 --- Comparison between the content-based similarity measure and the average inverse rank method --- p.69Chapter 3.6 --- Chapter summary --- p.73Chapter 4 --- Bilingual Event-Driven News Summarization --- p.74Chapter 4.1 --- Corpora --- p.75Chapter 4.2 --- Topic and event definitions --- p.76Chapter 4.3 --- Architecture of bilingual event-driven news summarization sys- tem --- p.77Chapter 4.4 --- Bilingual event-driven approach summarization --- p.80Chapter 4.4.1 --- Dictionary-based term translation applying on English news articles --- p.80Chapter 4.4.2 --- Preprocessing for Chinese news articles --- p.89Chapter 4.4.3 --- Event clusters generation --- p.89Chapter 4.4.4 --- Cluster selection and summary generation --- p.96Chapter 4.5 --- Evaluation for summarization based on event-driven approach --- p.101Chapter 4.6 --- Experimental results on event-driven summarization --- p.103Chapter 4.6.1 --- Experimental settings --- p.103Chapter 4.6.2 --- Results and analysis --- p.105Chapter 4.7 --- Chapter summary --- p.113Chapter 5 --- Applying Event-Driven Summarization to a Parallel Corpus --- p.114Chapter 5.1 --- Parallel corpus --- p.115Chapter 5.2 --- Parallel documents preparation --- p.116Chapter 5.3 --- Evaluation methods for the event-driven summaries generated from the parallel corpus --- p.118Chapter 5.4 --- Experimental results and analysis --- p.121Chapter 5.4.1 --- Experimental settings --- p.121Chapter 5.4.2 --- Results and analysis --- p.123Chapter 5.5 --- Chapter summary --- p.132Chapter 6 --- Conclusions and Future Work --- p.133Chapter 6.1 --- Conclusions --- p.133Chapter 6.2 --- Future work --- p.135Bibliography --- p.137Chapter A --- English Stop Word List --- p.144Chapter B --- Chinese Stop Word List --- p.149Chapter C --- Event List Items on the Corpora --- p.151Chapter C.1 --- "Event list items for the topic ""Upcoming Philippine election""" --- p.151Chapter C.2 --- "Event list items for the topic ""German train derail"" " --- p.153Chapter C.3 --- "Event list items for the topic ""Electronic service delivery (ESD) scheme"" " --- p.154Chapter D --- The sample of an English article (9505001.xml). --- p.15