280 research outputs found
Query-Based Summarization: A survey
This paper presents a survey of recent extractive query-based
summarization techniques. We explore approaches for single document
and multi-document 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
medical texts. The most recent developments in the field are presented
with opinion summarization of blog entries.This research is supported by the SmartBook project,
subsidized by the Bulgarian National Science Fund, under Grant D002-111
/15.12.2008
A survey on opinion summarization technique s for social media
The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization
TGSum: Build Tweet Guided Multi-Document Summarization Dataset
The development of summarization research has been significantly hampered by
the costly acquisition of reference summaries. This paper proposes an effective
way to automatically collect large scales of news-related multi-document
summaries with reference to social media's reactions. We utilize two types of
social labels in tweets, i.e., hashtags and hyper-links. Hashtags are used to
cluster documents into different topic sets. Also, a tweet with a hyper-link
often highlights certain key points of the corresponding document. We
synthesize a linked document cluster to form a reference summary which can
cover most key points. To this aim, we adopt the ROUGE metrics to measure the
coverage ratio, and develop an Integer Linear Programming solution to discover
the sentence set reaching the upper bound of ROUGE. Since we allow summary
sentences to be selected from both documents and high-quality tweets, the
generated reference summaries could be abstractive. Both informativeness and
readability of the collected summaries are verified by manual judgment. In
addition, we train a Support Vector Regression summarizer on DUC generic
multi-document summarization benchmarks. With the collected data as extra
training resource, the performance of the summarizer improves a lot on all the
test sets. We release this dataset for further research.Comment: 7 pages, 1 figure in AAAI 201
COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres
In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.This research has been supported by the project “Desarrollo de Técnicas Inteligentes e Interactivas de Minería de Textos” (PROMETEO/2009/119) and the project reference ACOMP/2011/001 from the Valencian Government, as well as by the Spanish Government (grant no. TIN2009-13391-C04-01)
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