Text Summarization of Czech News Articles Using Named Entities

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351127" target="_blank" >RIV/68407700:21230/21:00351127 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/21:00351127

Result on the web

<a href="https://doi.org/10.14712/00326585.012" target="_blank" >https://doi.org/10.14712/00326585.012</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14712/00326585.012" target="_blank" >10.14712/00326585.012</a>

Result language

angličtina

Original language name

Text Summarization of Czech News Articles Using Named Entities

Original language description

The foundation for the research of summarization in the Czech language was laid by the work of Straka et al. (2018). They published the SumeCzech, a large Czech news-based summarization dataset, and proposed several baseline approaches. However, it is clear from the achieved results that there is a large space for improvement. In our work, we focus on the impact of named entities on the summarization of Czech news articles. First, we annotate SumeCzech with named entities. We propose a new metric ROUGENE that measures the overlap of named entities between the true and generated summaries, and we show that it is still challenging for summarization systems to reach a high score in it. We propose an extractive summarization approach Named Entity Density that selects a sentence with the highest ratio between a number of entities and the length of the sentence as the summary of the article. The experiments show that the proposed approach reached results close to the solid baseline in the domain of news articles selecting the first sentence. Moreover, we demonstrate that the selected sentence reflects the style of reports concisely identifying to whom, when, where, and what happened. We propose that such a summary is beneficial in combination with the first sentence of an article in voice applications presenting news articles. We propose two abstractive summarization approaches based on Seq2Seq architecture. The first approach uses the tokens of the article. The second approach has access to the named entity annotations. The experiments show that both approaches exceed state-of-the-art results previously reported by Straka et al. (2018), with the latter achieving slightly better results on SumeCzech’s out-of-domain testing set.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

The Prague Bulletin of Mathematical linguistics

ISSN

0032-6585

e-ISSN

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Volume of the periodical

116

Issue of the periodical within the volume

April

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

21

Pages from-to

5-25

UT code for WoS article

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EID of the result in the Scopus database

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