Comparing Semantic Models for Evaluating Automatic Document Summarization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43926384" target="_blank" >RIV/49777513:23520/15:43926384 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-24033-6_29" target="_blank" >http://dx.doi.org/10.1007/978-3-319-24033-6_29</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-24033-6_29" target="_blank" >10.1007/978-3-319-24033-6_29</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing Semantic Models for Evaluating Automatic Document Summarization

Popis výsledku v původním jazyce

The main focus of this paper is the examination of semantic modelling in the context of automatic document summarization and its evaluation. The main area of our research is extractive summarization, more specifically, contrastive opinion summarization.And as it is with all summarization tasks, the evaluation of their performance is a challenging problem on its own. Nowadays, the most commonly used evaluation technique is ROUGE (Recall-Oriented Understudy for Gisting Evaluation). It includes measures (such as the count of overlapping n-grams or word sequences) for automatically determining the quality of summaries by comparing them to ideal human-made summaries. However, these measures do not take into account the semantics of words and thus, for example, synonyms are not treated as equal. We explore this issue by experimenting with various language models, examining their performance in the task of computing document similarity. In particular, we chose four semantic models (LSA, LDA,

Název v anglickém jazyce

Comparing Semantic Models for Evaluating Automatic Document Summarization

Popis výsledku anglicky

The main focus of this paper is the examination of semantic modelling in the context of automatic document summarization and its evaluation. The main area of our research is extractive summarization, more specifically, contrastive opinion summarization.And as it is with all summarization tasks, the evaluation of their performance is a challenging problem on its own. Nowadays, the most commonly used evaluation technique is ROUGE (Recall-Oriented Understudy for Gisting Evaluation). It includes measures (such as the count of overlapping n-grams or word sequences) for automatically determining the quality of summaries by comparing them to ideal human-made summaries. However, these measures do not take into account the semantics of words and thus, for example, synonyms are not treated as equal. We explore this issue by experimenting with various language models, examining their performance in the task of computing document similarity. In particular, we chose four semantic models (LSA, LDA,

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Text, Speech, and Dialogue

ISBN

978-3-319-24032-9

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

252-260

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Plzeň

Datum konání akce

14. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000365947800029