Benchmarks

This page documents performance of most popular contemporary NLP systems for Polish.

Single-word lemmatization and morphological analysis

System name and URL	Approach	Main publication	License	P	R	F
Morfeusz		Woliński, M. (2006). Morfeusz — a practical tool for the morphological analysis of Polish. In M.A. Kłopotek, S.T. Wierzchoń, K. Trojanowski (eds.) Proceedings of the International IIS:IIPWM 2006 Conference, pp. 511–520.	2-clause BSD	%	%	%
Morfologik		Miłkowski M. (2010). Developing an open-source, rule-based proofreading tool. Software: Practice and Experience, 40(7):543–566.		%	%	%
LemmaPL	dictionary-based rules and heuristics	Kobyliński Ł. (unpublished)	GPL	%	%	%

Multi-word lemmatization

System name and URL	Approach	Main publication	License	Accuracy
–	rule-based	Degórski, Ł. (2012). Towards the lemmatisation of Polish nominal syntactic groups using a shallow grammar. In P. Bouvry, M.A. Kłopotek, F. Leprevost, M. Marciniak, A. Mykowiecka, H. Rybiński (eds.) Security and Intelligent Information Systems, Lecture Notes in Computer Science vol. 7053, pp. 370–378, Springer-Verlag Berlin Heidelberg.	?	82.90%
–	automatic generation of lemmatization rules using CRF	Radziszewski A. (2013). Learning to lemmatise Polish noun phrases. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Volume 1: Long Papers. ACL, pp. 701–709.	GPL	80.70%
–	automatic generation of lemmatization rules from a corpus	Abramowicz W., Filipowska A., Małyszko J., Wagner T. (2015). Lemmatization of Multi-Word Entity Names for Polish Language Using Rules Automatically Generated Based on the Corpus Analysis. In Z. Vetulani, J. Mariani (eds.) Human Language Technologies as a Challenge for Computer Science and Linguistics, Fundacja Uniwersytetu im. A. Mickiewicza, pp. 540–544, Poznań.	?	82.10%
Polem	dictionary-based rules and heuristics	Marcińczuk M. (2017). Lemmatization of Multi-word Common Noun Phrases and Named Entities in Polish. In Proceedings of Recent Advances in Natural Language Processing, pp. 483–491.	GPL	97.99%

Disambiguated POS tagging

The comparisons are performed using plain text as input and reporting the accuracy lower bound (Acc_lower) metric proposed by Radziszewski and Acedański (2012). The metric penalizes all segmentation changes in regard to the gold standard and treats such tokens as misclassified. Furthermore, we report separate metric values for both known and unknown words to assess the performance of guesser modules built into the taggers. These are indicated as Acc^K_lower for known and Acc^U_lower for unknown words.

The experiments have been performed on the manually annotated part of the National Corpus of Polish v. 1.1 (1M tokens). The ten-fold cross-validation procedure has been followed, by re-evaluating the methods ten times, each time selecting one of ten parts of the corpus for testing and the remaining parts for training the taggers. The provided results are averages calculated over ten training and testing sequences. Each of the taggers and each tagger ensemble has been trained and tested on the same set of cross-validation folds, so the results are directly comparable. Each of the training folds has been reanalyzed, according to the procedure described in (A Tiered CRF Tagger for Polish, using the Maca toolkit (Radziszewski and Śniatowski 2011). The idea of a morphological reanalysis of the gold-standard data is to allow the trained tagger to see similar input that is expected in the tagging phase. The training data is firstly turned into plain text and analyzed using the same mechanism that will be used by the tagger during actual tagging process. The output of the analyzer is then synchronized with the original gold-standard data, by using the original tokenization. Tokens with changed segmentation are taken from the gold-standard intact. In the case of tokens for which the segmentation did not change in the process of morphological analysis, the produced interpretations are compared with the original. A token is marked as an unknown word if the correct interpretation has not been produced by the analyzer. Maca has been run with the morfeusz-nkjp-official configuration, which uses Morfeusz SGJP analyzer (Woliński 2006) and no guesser module.

Tagger efficiency was compared by measuring training and tagging times of each of the methods on the same machine. 1.1M token set was used both for training and tagging stages. The total processing time included model loading/saving time and other I/O operations (e.g. reading/writing the tokens).

System name and URL	Approach	Main publication	License	Acc_lower	Acc^K_lower	Acc^U_lower	Training time	Tagging time
OpenNLP	MaxEnt model	Kobyliński Ł., Kieraś W. (2016). Part of Speech Tagging for Polish: State of the Art and Future Perspectives. In Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2016), Konya, Turkey.	GPL	87.24%	88.02%	62.05%	11095 s	362 s
Pantera	rule-based adapted Brill tagger	Acedański S. (2010). A Morphosyntactic Brill Tagger for Inflectional Languages. In H. Loftsson, E. Rögnvaldsson, S. Helgadóttir (eds.) Advances in Natural Language Processing, LNCS 6233, pp. 3–14, Springer.	GPL 3	88.95%	91.22%	15.19%	2624 s	186 s
WMBT	memory-based	Radziszewski A., Śniatowski T. (2011). A memory-based tagger for Polish. In: Z. Vetulani (ed.) Proceedings of the 5th Language and Technology Conference (LTC 2011), pp. 556–560, Poznań, Poland.		90.33%	91.26%	60.25%	548 s	4338 s
WCRFT	tiered, CRF-based	Radziszewski A. (2013). A Tiered CRF Tagger for Polish. In R. Bembenik, Ł. Skonieczny, H. Rybiński, M. Kryszkiewicz, M. Niezgódka (eds.) Intelligent Tools for Building a Scientific Information Platform, pp. 215–230, Springer Berlin Heidelberg.	LGPL 3.0	90.76%	91.92%	53.18%	27242 s	420 s
Concraft	mutually dependent CRF layers	Waszczuk J. (2012). Harnessing the CRF complexity with domain-specific constraints. The case of morphosyntactic tagging of a highly inflected language. In Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), pp. 2789–2804, Mumbai, India.	2-clause BSD	91.07%	92.06%	58.81%	26675 s	403 s
PoliTa	voting ensemble	Kobyliński Ł. (2014). PoliTa: A multitagger for Polish. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, S. Piperidis (eds.) Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), pp. 2949–2954, Reykjavík, Iceland, ELRA.	GPL	92.01%	92.91%	62.81%	N/A	N/A
Toygger	bi-LSTM	Krasnowska-Kieraś K. (2017). Morphosyntactic disambiguation for Polish with bi-LSTM neural networks. In Zygmunt Vetulani and Patrick Paroubek (eds.) Proceedings of the 8th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 367–371, Poznań, Poland. Fundacja Uniwersytetu im. Adama Mickiewicza w Poznaniu.	GPL	92.01%	92.91%	62.81%	N/A	N/A
KRNNT	RNN	Wróbel K. (2017) KRNNT: Polish Recurrent Neural Network Tagger. In Zygmunt Vetulani and Patrick Paroubek (eds.) Proceedings of the 8th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 386–391, Poznań, Poland. Fundacja Uniwersytetu im. Adama Mickiewicza w Poznaniu.	LGPL 3.0	93.72%	94.43%	69.03%	245.9 s (GeForce GTX 1050M)	N/A

Dependency parsing

Dependency parsing systems are trained and tested either on Polish Dependency Bank (PDB) or on PDB-UD, i.e. PDB converted to the Universal Dependencies format (license CC BY-NC-SA 4.0).

Parser	Model	Data	External Embedding	Evaluation script	UPOS F1	XPOS F1	UFeats F1	AllTags	Lemmas F1	UAS F1	LAS F1	CLAS F1	MLAS F1	BLEX F1	SLAS
COMBO	COMBO-SP	PDB	FastText	poleval						93.08	89.41	88.64	88.22	88.64
COMBO-pytorch	COMBO2-SP	PDB	HerBERT-base	poleval						94.99	91.74	91.04	90.60	91.04
COMBO-pytorch	COMBO2-SP	PDB	HerBERT-large	poleval						95.29	92.03	91.24	90.83	91.24

COMBO	COMBO-MSP	PDB	FastText	poleval	94.26	94.26	94.13	93.49	97.29	91.13	86.33	85.33	78.74	82.86
COMBO-pytorch	COMBO2-MSP	PDB	HerBERT-base	poleval	99.02	96.55	96.69	96.23	97.75	94.10	90.66	90.13	86.02	87.84
COMBO-pytorch	COMBO2-MSP	PDB	HerBERT-large	poleval	99.08	96.78	96.92	96.47	97.77	94.40	90.99	90.32	86.39	88.01
COMBO	COMBO-SMSP	PDB	FastText	poleval	98.47	93.96	93.72	92.98	97.29	91.24	86.43	85.53	78.80	83.03	72.12
COMBO-pytorch	COMBO2-SMSP	PDB	HerBERT-base	poleval	99.04	96.54	96.64	96.19	97.64	94.15	90.64	90.04	85.94	87.61	85.71

UDPipe	UDPipe-MSP	PDB-UD	FastText	conll	97.34	88.62	89.16	88.03	96.08	87.41	83.94	80.43	69.98	76.49
				poleval	97.30	88.61	89.00	87.96	96.04	87.41	80.75	75.48	65.92	71.83
COMBO	COMBO-MSP	PDB-UD	FastText	conll	98.56	94.62	94.63	93.39	97.58	93.88	91.61	89.37	82.48	86.66
				poleval	98.56	94.62	94.45	93.27	97.58	93.88	88.85	85.04	78.55	82.60
COMBO-pytorch	COMBO2-MSP	PDB-UD	HerBERT-base	conll	99.02	96.42	96.73	95.73	97.77	96.07	94.52	93.17	88.41	90.30
				poleval	99.02	96.42	96.64	95.67	97.77	96.07	92.19	89.52	85.04	86.83
COMBO-pytorch	COMBO2-MSP	PDB-UD	HerBERT-large	conll	99.07	96.73	97.01	96.04	97.83	95.85	94.27	92.84	88.45	90.09
				poleval	99.07	96.73	96.91	95.99	97.83	95.85	91.93	89.16	84.94	86.55
COMBO	COMBO-SMSP	PDB-UD	FastText	poleval	98.48	94.64	94.36	93.14	97.43	93.81	88.84	85.08	78.44	82.43	69.64
COMBO-pytorch	COMBO2-SMSP	PDB-UD	HerBERT-base	poleval	98.99	96.42	96.64	95.67	97.74	95.95	92.07	89.28	84.72	86.60	84.32

SP (syntactic prediction) -- prediction of labelled dependency trees

MSP (morphosyntactic prediction) -- prediction of morphosyntactic features (i.e. LEMMA, UPOS, XPOS, FEATS) and labelled dependency trees

SMSP (semantic and morphosyntactic prediction) -- prediction of morphosyntactic features (i.e. LEMMA, UPOS, XPOS, FEATS), labelled dependency trees and semantic roles

conll -- The script (conll18_ud_eval.py) of CoNLL 2018 shared task is used for evaluation (only UD labels are taken into account).

poleval -- The script(poleval2018_cykle.py) of PolEval 2018 shared task on dependency parsing is used for evaluation (UD labels and Polish-specific sublabels are taken into account).

System name and URL	Model	License	UAS	LAS	MLAS	BLEX	ELAS
MaltParser	SP		83.39	80.39	n/a	n/a	n/a
UDPipe	SP		83.41	80.13	n/a	n/a	n/a
transition-based BIST	SP		86.77	83.35	n/a	n/a	n/a
graph-based BIST	SP		87.01	83.67	n/a	n/a	n/a
MATE tool	SP		89.51	87.12	n/a	n/a	n/a
COMBO	SP*		91.25	88.81	n/a	n/a	n/a
COMBO	SP		91.36	88.92	n/a	n/a	80.65
COMBO	SP*+embedd		91.84	89.56	n/a	n/a	n/a
COMBO	SP+embedd		91.87	89.58	n/a	n/a	81.22
Stanford system	SP		92.78	90.61	n/a	n/a	n/a
UDPipe	MSP		80.95	76.11	61.34	67.82	n/a
UDPipe	MSP+embedd		82.89	78.57	63.88	70.49	n/a
MATE tool	MSP		85.56	81.34	65.31	69.64	n/a
COMBO	MSP		90.20	86.64	73.90	79.94	n/a
COMBO	MSP+embedd		91.17	87.89	76.63	81.86	n/a
Stanford system	MSP+embedd		91.65	88.70	78.26	62.67	n/a
Stanford system	MSP		91.70	88.88	78.36	62.68	n/a

SP (syntactic prediction) -- prediction of labelled dependency trees by a model trained on PDBUD

SP* (syntactic prediction) -- prediction of labelled dependency trees by a model trained on PDB

MSP (morphosyntactic prediction) -- prediction of morphosyntactic features (i.e. LEMMA, UPOS, XPOS, FEATS) and labelled dependency trees by a model trained on PDBUD

+embedd -- model estimation supported by external word embeddings

Related publications

Wróblewska A. and Rybak P. (2019) Dependency parsing of Polish. Poznań Studies in Contemporary Linguistics, 55(2):305–337. (Note: Please contact the first author to get a copy of this article.)
Wróblewska A. (2014). Polish Dependency Parser Trained on an Automatically Induced Dependency Bank. Ph.D. dissertation, Institute of Computer Science, Polish Academy of Sciences, Warsaw, 2014.
Wróblewska A. (2018). Results of the PolEval 2018 Competition: Dependency Parsing Shared Task. In: Ogrodniczuk M. and Kobyliński Ł. (eds.) Proceedings of the PolEval 2018 Workshop. Institute of Computer Science, Polish Academy of Sciences, pp. 11–24.
Wróblewska A. (2018). Extended and Enhanced Polish Dependency Bank in Universal Dependencies Format In Proceedings of Universal Dependencies Workshop 2018 (UDW 2018).

Shallow parsing

System name and URL	Approach	Main publication	License	P	R	F
Spejd	rule-based	Buczyński A., Przepiórkowski A. (2009). Spejd: A shallow processing and morphological disambiguation tool. In Z. Vetulani, H. Uszkoreit (eds.) Human Language Technology: Challenges of the Information Society, LNCS 5603, pp. 131–141. Springer-Verlag, Berlin.	GPL 3	%	%	%

Word sense disambiguation

Manually annotated subcorpus of the National Corpus of Polish with 3889 texts and 1217822 segments was used for training and test (cross-validation); it contained 34186 occurrences of multi-sense words (1–7 senses). Simple heuristics selecting the most frequent sense resulted in 78.3% accuracy. The table presents results of leave-one-out evaluation with individual selection of each ambiguous words (as described in the publication).

System name and URL	Approach	Main publication	License	Accuracy
WSDDE	Machine-learning	Kopeć M., Młodzki R., Przepiórkowski A. (2012). Automatyczne znakowanie sensami słów. In A. Przepiórkowski, M. Bańko, R.L. Górski, B. Lewandowska-Tomaszczyk (eds.) Narodowy Korpus Języka Polskiego, pp. 209–224. Wydawnictwo Naukowe PWN, Warsaw.	GPL 3	91.46%

Named entity recognition

The below table reflects PolEval 2018 Named Entity Recognition task plus several new systems made available more recently; please see the training data – 1M NKJP corpus and testing data with evaluation script.

System name and URL	Approach	Main publication	Weighted F₁
NER model for SpacyPL		Tuora R., Kobyliński Ł. (2019). Integrating Polish Language Tools and Resources in spaCy. Proceedings of PP-RAI 2019 Conference, pp. 210–214.	0.8752
Per group LSTM-CRF with C. S. E.	Per group LSTM-CRF with Contextual String Embeddings		0.851
PolDeepNer	BiDirectional GRU/LSTM with CRF	Marcińczuk M., Kocoń J., Gawor., M. (2018). Recognition of Named Entities for Polish-Comparison of Deep Learning and Conditional Random Fields Approaches. Proceedings of PolEval 2018 Workshop.	0.866
Liner2	CRF	Marcińczuk M., Kocoń J., Oleksy., M. (2017). Liner2 — a Generic Framework for Named Entity Recognition Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing.	0.810
OPI Z3	???		0.793
joint	???		0.780
disjoint	???		0.779
via ner	LSTM+CRF		0.756
NERF + polimorf	CRF	Savary A., Waszczuk J., Przepiórkowski A. Towards the annotation of named entities in the National Corpus of Polish. Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010), pp. 3622–3629. ELRA. See also Chapters 9 and 13 in the NKJP book (in Polish).	0.739
NERF	CRF		0.735
kner sep	???		0.733
Poleval2k18	???		0.719
KNER	RNN/CNN + CRF		0.711
simple ner	BiDirectional GRU		0.636

Sentiment analysis

System name and URL	Approach	Main publication	License	Accuracy
Sentipejd	dictionary+rules	Buczyński A., Wawer A. (2008). Shallow parsing in sentiment analysis of product reviews. In S. Kübler, J. Piskorski, A. Przepiórkowski (eds.) Proceedings of the LREC 2008 Workshop on Partial Parsing: Between Chunking and Deep Parsing, pp. 14–18, Marrakech, ELRA.	API usage	0.782*

* measured document level with a C.50 classifier

The systems below have been evaluated on PolEval 2017 test data, using the Polish sentiment treebank version 1.0.

System name and URL	Approach	Main publication	License	Accuracy
Tree-LSTM-NR	Tree-LSTM	Ryciak N. (2017). Polish Language Sentiment Analysis with Tree-Structured Long Short-Term Memory Network. In Z. Vetulani and P. Paroubek (eds.) Proceedings of the 8th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 402–405, Poznań, Poland. Fundacja Uniwersytetu im. Adama Mickiewicza w Poznaniu.	Open source	0.795
Alan Turing climbs a tree	Tree-LSTM	Żak P., Korbak T. (2017). Fine-tuning Tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank. In Z. Vetulani and P. Paroubek (eds.) Proceedings of the 8th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 392–396, Poznań, Poland. Fundacja Uniwersytetu im. Adama Mickiewicza w Poznaniu.	Open source	0.805
Tree-LSTM-ML	Tree-LSTM	Lew M., Pęzik P. (2017). A Sequential Child-Combination Tree-LSTM Network for Sentiment Analysis. In Z. Vetulani and P. Paroubek (eds.) Proceedings of the 8th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 397–401, Poznań, Poland. Fundacja Uniwersytetu im. Adama Mickiewicza w Poznaniu.	Open source	0.768

Mention detection

Precision, recall and F-measure are calculated on Polish Coreference Corpus data with two alternative mention detection scores:

EXACT: score of exact boundary matches (an automatic and a manual mention match if they have exactly the same boundaries; in other words, they consist of the same tokens)
HEAD: score of head matches (we reduce the automatic and the manual mentions to their single head tokens and compare them).

System name and URL	Approach	Main publication	License	EXACT			HEAD
System name and URL	Approach	Main publication	License	P	R	F	P	R	F
MentionDetector	rule-based	Ogrodniczuk M., Głowińska K., Kopeć M., Savary A., Zawisławska M. (2015). Coreference in Polish: Annotation, Resolution and Evaluation, chapter 10.6. Walter De Gruyter.	CC BY 3	70.11%	68.13%	69.10%	90.07%	88.21%	89.12%
MentionStat	statistical	Ogrodniczuk M. (2019). Automatyczne wykrywanie nominalnych zależności referencyjnych w języku polskim. Wydawnictwa Uniwersytetu Warszawskiego.	CC BY 3	74.34%	69.41%	71.79%	92.27%	90.21%	91.23%

Coreference resolution

As there is still no consensus about the single best coreference resolution metrics, CoNLL measure is used to rank systems (average of MUC, B3 and CEAFE F1) with a CoNLL-2011 shared task-based approach with two calculation strategies for EXACT mention borders and semantic HEADs:

INTERSECT: consider only correct system mentions (i.e. the intersection between gold and system mentions)
TRANSFORM: unify system and gold mention sets using the following procedure for twinless mentions (without a corresponding mention in the second set):
1. insert twinless gold mentions into system mention set as singletons
2. remove twinless singleton system mentions
3. insert twinless non-singletion system mentions into gold set as singletons.

The results are produced on Polish Coreference Corpus data, with 10-fold crossvalidation.

System name and URL	Approach	Main publication	License	GOLD	EXACT INTERSECT	EXACT TRANSFORM	HEAD INTERSECT	HEAD TRANSFORM
Ruler	rule-based	Ogrodniczuk M., Kopeć M. (2011). End-to-end coreference resolution baseline system for Polish. In Z. Vetulani (ed.), Proceedings of the 5th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 167–171, Poznań, Poland.	CC BY 3	74.10%	78.86%	68.88%	77.05%	72.19%
Bartek5	statistical	Kopeć M., Ogrodniczuk M. (2012). Creating a Coreference Resolution System for Polish. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012), pp. 192–195, ELRA.	CC BY 3	80.50%	82.71%	73.96%	80.00%	74.82%
BartekS2	sieve-based	Nitoń B., Ogrodniczuk M. (2017). Multi-Pass Sieve Coreference Resolution System for Polish. In J. Gracia, F. Bond, J. P. McCrae, P. Buitelaar, Ch. Chiarcos, S. Hellmann (eds.) Proceedings of the 1st Conference on Language, Data and Knowledge (LDK 2017).	CC BY 3	80.70%	82.49%	73.21%	80.85%	75.55%
Corneferencer	neural	Nitoń B., Morawiecki P., Ogrodniczuk M. (2018). Deep neural networks for coreference resolution for Polish. In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, T. Tokunaga (eds.) Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018), pp. 395–400, ELRA.	CC BY 3	80.59%	82.73%	73.39%	80.89%	76.03%
Hybrid	hybrid	Ogrodniczuk M. (2019). Automatyczne wykrywanie nominalnych zależności referencyjnych w języku polskim. Wydawnictwa Uniwersytetu Warszawskiego.	CC BY 3	81.09%	82.54%	73.17%	81.04%	75.74%

Summarization

The table presents results of evaluation on the Polish Summaries Corpus (154 texts, each with 5 abstractive summaries, 20% word count of the original document). ROUGE covers several metrics, with the following variants used here:

ROUGE_n which counts n-gram co-occurrences between reference (gold) summaries and system summary,
ROUGE-M_n with ROUGE_n score calculated for each text using a single manual summary which gives the highest score as a reference.

The evaluation tool source code is available here.

System name and URL	Approach	Main publication	License	ROUGE₁	ROUGE₂	ROUGE₃	ROUGE-M₁	ROUGE-M₂	ROUGE-M₃
PolSum	?	Ciura M., Grund D., Kulików S., Suszczańska N. (2004). A System to Adapt Techniques of Text Summarizing to Polish. In Okatan A. (ed.) International Conference on Computational Intelligence, pp. 117–120, Istanbul, Turkey. International Computational Intelligence Society.	?	? %	? %	? %	? %	? %	? %
Open Text Summarizer	word frequency based sentence extraction	--	GNU GPL 2.0	51.3%	13.6%	9.0%	58.5%	22.5%	17.9%
Emily-C	sentence extraction with machine learning	Kopeć M. (2015). Coreference-based Content Selection for Automatic Summarization of Polish News. ITRIA 2015. Selected Problems in Information Technologies, pp. 23–46.	GNU GPL 3.0	52.9%	15.1%	10.0%	58.8%	24.2%	19.2%
Emily-S	sentence extraction with machine learning		GNU GPL 3.0	53.0%	15.4%	10.4%	59.4%	24.7%	20.1%
TextRank	unsupervised, graph based sentence extraction	Barrios, F., López, F., Argerich, L., Wachenchauzer, R. (2015). Variations of the Similarity Function of TextRank for Automated Summarization. arXiv:1602.03606.	MIT	56.5%	19.2%	12.8%	63.3%	30.0%	23.8%
Lakon	sentence extraction relying on one of: positional heuristics, word frequency features, lexical chains information	Dudczak A. (2007). Zastosowanie wybranych metod eksploracji danych do tworzenia streszczeń tekstów prasowych dla języka polskiego. MSc thesis, Poznań Technical University.	3-clause BSD	55.4%	20.8%	14.4%	62.9%	33.3%	27.4%
Summarizer	sentence extraction with machine learning	Świetlicka J. (2010). Metody maszynowego uczenia w automatycznym streszczaniu tekstów. MSc thesis, Warsaw University.	GNU GPL 3.0	58.0%	22.6%	16.1%	65.4%	35.8%	29.8%
Nicolas	sentence extraction with machine learning	Kopeć M. (2018). Summarization of Polish Press Articles Using Coreference PhD dissertation.	GNU GPL 3.0	59.1%	24.2%	17.5%	67.9%	39.8%	33.9%

Language models

Given a set of sentences in Polish in a random order, each with original punctuation, the goal of the task is to create a language model for Polish. Please see segmented training data and segmented testing data. Evaluation is based on calculation of perplexity, please see instruction on OOV rate and perplexity.

System name and URL	Main publication	Perplexity
ULMFiT-SP-PL	Kardas M., Howard J., Czapla P. (2018). Universal Language Model Fine-Tuning with Subword Tokenization for Polish.	117.6705
AGH-UJ	Please see the model.	146.7082
PocoLM Order 6		208.6297

UDPipe	SP	PDB-UD
COMBO	MSP	PDB-UD	word2vec	98.55	94.44	94.39	93.13	97.56	93.86	91.60	89.46	82.37	86.79

benchmarks

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