automatic import of python-yake

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+/yake-0.4.8.tar.gz
diff --git a/python-yake.spec b/python-yake.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-yake
+Version:	0.4.8
+Release:	1
+Summary:	Keyword extraction Python package
+License:	LGPLv3
+URL:		https://pypi.python.org/pypi/yake
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/7a/95/b4091038c7fa99408f0878070cf11f6b4d6d2675461b7e80848482608c52/yake-0.4.8.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-tabulate
+Requires:	python3-click
+Requires:	python3-numpy
+Requires:	python3-segtok
+Requires:	python3-networkx
+Requires:	python3-jellyfish
+
+%description
+
+# Yet Another Keyword Extractor (Yake)
+
+Unsupervised Approach for Automatic Keyword Extraction using Text Features.
+
+YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Experimental results carried out on top of twenty datasets (see Benchmark section below) show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. In addition to the python package here described, we also make available a <a href="http://yake.inesctec.pt" target="_blank">demo</a>, an <a href="http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords" target="_blank">API</a> and a <a href="https://play.google.com/store/apps/details?id=com.yake.yake" target="_blank">mobile app</a>.
+
+## Main Features
+
+* Unsupervised approach
+* Corpus-Independent
+* Domain and Language Independent
+* Single-Document
+
+## Where can I find YAKE!?
+YAKE! is available online [http://yake.inesctec.pt], as an open source Python package [https://github.com/LIAAD/yake] and on [Google Play](https://play.google.com/store/apps/details?id=com.yake.yake).
+
+## References
+Please cite the following works when using YAKE
+
+<b>In-depth journal paper at Information Sciences Journal</b>
+
+Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. [pdf](https://doi.org/10.1016/j.ins.2019.09.013)
+
+<b>ECIR'18 Best Short Paper</b>
+
+Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63)
+
+Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80)
+
+## Awards
+[ECIR'18](http://ecir2018.org) Best Short Paper
+
+
+
+
+%package -n python3-yake
+Summary:	Keyword extraction Python package
+Provides:	python-yake
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-yake
+
+# Yet Another Keyword Extractor (Yake)
+
+Unsupervised Approach for Automatic Keyword Extraction using Text Features.
+
+YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Experimental results carried out on top of twenty datasets (see Benchmark section below) show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. In addition to the python package here described, we also make available a <a href="http://yake.inesctec.pt" target="_blank">demo</a>, an <a href="http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords" target="_blank">API</a> and a <a href="https://play.google.com/store/apps/details?id=com.yake.yake" target="_blank">mobile app</a>.
+
+## Main Features
+
+* Unsupervised approach
+* Corpus-Independent
+* Domain and Language Independent
+* Single-Document
+
+## Where can I find YAKE!?
+YAKE! is available online [http://yake.inesctec.pt], as an open source Python package [https://github.com/LIAAD/yake] and on [Google Play](https://play.google.com/store/apps/details?id=com.yake.yake).
+
+## References
+Please cite the following works when using YAKE
+
+<b>In-depth journal paper at Information Sciences Journal</b>
+
+Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. [pdf](https://doi.org/10.1016/j.ins.2019.09.013)
+
+<b>ECIR'18 Best Short Paper</b>
+
+Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63)
+
+Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80)
+
+## Awards
+[ECIR'18](http://ecir2018.org) Best Short Paper
+
+
+
+
+%package help
+Summary:	Development documents and examples for yake
+Provides:	python3-yake-doc
+%description help
+
+# Yet Another Keyword Extractor (Yake)
+
+Unsupervised Approach for Automatic Keyword Extraction using Text Features.
+
+YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Experimental results carried out on top of twenty datasets (see Benchmark section below) show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. In addition to the python package here described, we also make available a <a href="http://yake.inesctec.pt" target="_blank">demo</a>, an <a href="http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords" target="_blank">API</a> and a <a href="https://play.google.com/store/apps/details?id=com.yake.yake" target="_blank">mobile app</a>.
+
+## Main Features
+
+* Unsupervised approach
+* Corpus-Independent
+* Domain and Language Independent
+* Single-Document
+
+## Where can I find YAKE!?
+YAKE! is available online [http://yake.inesctec.pt], as an open source Python package [https://github.com/LIAAD/yake] and on [Google Play](https://play.google.com/store/apps/details?id=com.yake.yake).
+
+## References
+Please cite the following works when using YAKE
+
+<b>In-depth journal paper at Information Sciences Journal</b>
+
+Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. [pdf](https://doi.org/10.1016/j.ins.2019.09.013)
+
+<b>ECIR'18 Best Short Paper</b>
+
+Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63)
+
+Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80)
+
+## Awards
+[ECIR'18](http://ecir2018.org) Best Short Paper
+
+
+
+
+%prep
+%autosetup -n yake-0.4.8
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-yake -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.8-1
+- Package Spec generated
diff --git a/sources b/sources
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+02eaafd91a226f18398b53b11a8fb120  yake-0.4.8.tar.gz