SlowGuess/NAACL_2022_OCR huggingface dataset:
Sarthak Jain, Edward Banner, Jan-Willem van de Meent, Iain J. Marshall, and Byron C. Wallace. 2018. Learning disentangled representations of texts with application to biomedical abstracts. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4683-4693, Brussels, Belgium. Association for Computational Linguistics.
Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over BERT. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, pages 39-48. ACM.
Su Nam Kim, David Martinez, Lawrence Cavedon, and Lars Yencken. 2011. Automatic classification of sentences to support evidence based medicine. In BMC bioinformatics, volume 12, pages 1-10. BioMed Central.
Yuta Kobayashi, Masashi Shimbo, and Yuji Matsumoto. 2018. Citation recommendation using distributed representation of discourse facets in scientific articles. In Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, JCDL '18, page 243-251, New York, NY, USA. Association for Computing Machinery.
Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, and Killian Q. Weinberger. 2015. From word embeddings to document distances. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, volume 37 of JMLR Workshop and Conference Proceedings, pages 957-966. JMLR.org.
Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S Weld, et al. 2022. A search engine for discovery of scientific challenges and directions. AAAI.
Jinhyuk Lee, Alexander Wettig, and Danqi Chen. 2021. Phrase retrieval learns passage retrieval, too. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3661-3672, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019. Latent retrieval for weakly supervised open domain question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6086-6096, Florence, Italy. Association for Computational Linguistics.
Jimmy Lin, Rodrigo Nogueira, and Andrew Yates. 2021. Pretrained transformers for text ranking: Bert and beyond. Synthesis Lectures on Human Language Technologies, 14(4):1-325.
Matteo Lissandrini, Davide Mottin, Themis Palpanas, and Yannis Velegrakis. 2019. Example-based search: a new frontier for exploratory search. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019, pages 1411-1412. ACM.
Zhenghao Liu, Chenyan Xiong, Maosong Sun, and Zhiyuan Liu. 2018. Entity-duet neural ranking: Understanding the role of knowledge graph semantics in neural information retrieval. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2395-2405, Melbourne, Australia. Association for Computational Linguistics.
Kyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kinney, and Daniel Weld. 2020. S2ORC: The semantic scholar open research corpus. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4969-4983, Online. Association for Computational Linguistics.
Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins. 2021. Sparse, dense, and attentional representations for text retrieval. Transactions of the Association for Computational Linguistics, 9:329-345.
Kelvin Luu, Xinyi Wu, Rik Koncel-Kedzierski, Kyle Lo, Isabel Cachola, and Noah A. Smith. 2021. Explaining relationships between scientific documents. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2130-2144, Online. Association for Computational Linguistics.
David Mimno and Andrew McCallum. 2007. Expertise modeling for matching papers with reviewers. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 500-509.
Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to match using local and distributed representations of text for web search. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3-7, 2017, pages 1291-1299. ACM.
Shikhar Murty, Pang Wei Koh, and Percy Liang. 2020. ExpBERT: Representation engineering with natural language explanations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2106-2113, Online. Association for Computational Linguistics.
Sheshera Mysore, Tim O'Gorman, Andrew McCallum, and Hamed Zamani. 2021. CSFCube - a test collection of computer science research articles for faceted query by example. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
Mariana Neves, Daniel Butzke, and Barbara Grune. 2019. Evaluation of scientific elements for text similarity in biomedical publications. In Proceedings of the 6th Workshop on Argument Mining, pages 124-135, Florence, Italy. Association for Computational Linguistics.
Allen Nie, Arturo L. Pineda, Matt W. Wright, Hannah Wand, Bryan Wulf, Helio A. Costa, Ronak Y. Patel, Carlos D. Bustamante, and James Zou. 2020. LitGen: Genetic Literature Recommendation Guided by Human Explanations, pages 67-78.
Malte Ostendorff, Nils Rethmeier, Isabelle Augenstein, Bela Gipp, and Georg Rehm. 2022. Neighborhood contrastive learning for scientific document representations with citation embeddings.
Malte Ostendorff, Terry Ruas, Till Blume, Bela Gipp, and Georg Rehm. 2020. Aspect-based document similarity for research papers. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6194-6206, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Gabriel Peyre, Marco Cuturi, et al. 2019. Computational optimal transport: With applications to data science. Foundations and Trends® in Machine Learning, 11(5-6):355-607.
Nils Reimers and Iryna Gurevych. 2019. SentenceBERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982-3992, Hong Kong, China. Association for Computational Linguistics.
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and TieYan Liu. 2020. Mpnet: Masked and permuted pretraining for language understanding. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.
Yale Song and Mohammad Soleymani. 2019. *Polysemous visual-semantic embedding for cross-modal retrieval*. In *IEEE Conference on Computer Vision and Pattern Recognition*, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pages 1979-1988. Computer Vision Foundation / IEEE.
Kyle Swanson, Lili Yu, and Tao Lei. 2020. Rationalizing text matching: Learning sparse alignments via optimal transport. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5609-5626, Online. Association for Computational Linguistics.
Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, and Andrew McCallum. 2019. Optimal transport-based alignment of learned character
representations for string similarity. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5907-5917, Florence, Italy. Association for Computational Linguistics.
Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R. Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: Constructing a pandemic information retrieval test collection. SIGIR Forum.
Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Michael Kinney, Yunyao Li, Ziyang Liu, William Merrill, Paul Mooney, Dewey A. Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, Alex D. Wade, Kuansan Wang, Nancy Xin Ru Wang, Christopher Wilhelm, Boya Xie, Douglas M. Raymond, Daniel S. Weld, Oren Etzioni, and Sebastian Kohlmeier. 2020. CORD-19: The COVID-19 open research dataset. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.
Yining Wang, Liwei Wang, Yuanzhi Li, Di He, and Tie-Yan Liu. 2013. A theoretical analysis of NDCG type ranking measures. In COLT 2013 - The 26th Annual Conference on Learning Theory, June 12-14, 2013, Princeton University, NJ, USA, volume 30 of JMLR Workshop and Conference Proceedings, pages 25-54. JMLR.org.
Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, and Justin M. Solomon. 2019. Hierarchical optimal transport for document representation. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 1599-1609.
Hamed Zamani, Mostafa Dehghani, W. Bruce Croft, Erik G. Learned-Miller, and Jaap Kamps. 2018. From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, pages 497-506. ACM.
Xuhui Zhou, Nikolaos Pappas, and Noah A. Smith. 2020. Multilevel text alignment with cross-document attention. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5012-5025, Online. Association for Computational Linguistics.
A Co-citation Data
As noted in §3.1, we train the proposed methods on English co-cited papers. We build a dataset
of co-cited papers from the S2ORC corpus $^5$  (Lo et al., 2020). Since our evaluation datasets draw on text from different domains we build training sets with co-cited papers for each: biomedicine for RELISH and TRECCOVID $_{\text{RF}}$ , computer science for CSFC $_{\text{UBE}}$ , and a 60/40 mix of biomedicine and CS for SciDOCS. Each dataset contains 1.3M training triples.
Next we describe construction of our co-citation data given 8.1 million English full text articles in the S2ORC corpus which have been parsed for citation mentions and linked to cited papers in the corpus using automatic tools (Lo et al., 2020):
- Domain definition: We define our biomedical articles to be those tagged either "Medicine" or "Biology" in S2ORC. "Computer Science" tagged papers are treated as CS papers.
- Co-citation contexts: To obtain co-cotation contexts - we first obtain sentence boundaries for co-citation contexts using the en_core_ - sci_sm pipeline included in spacy.
- Filtering abstracts: In selecting abstracts for our dataset we retain those that have a minimum of 3 sentences, and a maximum of 20 sentences. Further, abstracts where all the sentences are too small (3 tokens) are excluded. Similarly, abstracts with sentences greater than 80 tokens are excluded.
- Selecting training co-cited abstract data  $\{\mathcal{P}\}$ : Given contexts with qualifying abstracts as described above, we only retain co-citation contexts with 2 or 3 co-cited papers. A manual examination revealed that larger co-cited sets tended to be more loosely related.
- Selecting co-citation sentence training data for  $\mathrm{BERT}_{\mathcal{E}}$ : Note that this represents a sentence encoder trained by treating co-citation contexts referencing the same paper as paraphrases. Here, we select co-citation contexts containing 2 or more co-cited papers as paraphrase sets  $\mathcal{E}$ .
Abstract level training triples for the biomedical and computer science sets are built by treating all unique pairs of papers as positives. 1.3 million triples were used for each domain - these were sampled from larger sets at random.
B Evaluation Dataset Details
Here we provide further detail on the evaluation datasets overviewed in  $\S 4$
RELISH: An annotated dataset of biomedical abstract queries labelled by experts (Brown et al., 2019). In a number of cases, expert annotators are the authors of query papers. Per query candidate pools are of size 60, with 1638 queries in development and test sets each. Dataset is released under a Creative Commons Attribution 4.0 International License.
TRECCOVID<sub>RF</sub>: While the original TRECCOVID dataset of Voorhees et al. (2021) is labelled for ad-hoc search by experts, we reformulate the dataset for abstract similarity, treating all documents relevant to one ad-hoc query as similar to each other. From each original query and its respective relevance-labeled documents, we sample an abstract from relevant documents (relevance of 2) and use that as our query document. We treat all other relevant documents as positive examples for the query. Documents relevant for other queries are treated as irrelevant for the sampled query. This results in about 9000 candidates per query abstract in TRECCOVID<sub>RF</sub>. TRECCOVID<sub>RF</sub> consists of about 1200 queries in the development and test splits each. This dataset builds on the CORD-19 dataset (Wang et al., 2020) released under a Apache License 2.0, the license of TRECCOVID however isn't clear from the dataset release.
SCIDOCs: A benchmark suite of tasks intended for abstract-level scientific document representations (Cohan et al., 2020). We evaluate our methods on the tasks of predicting: citations, co-citations, co-views, and co-reads. Per query candidate pools are of size 30 about 1000 queries per task and development and test split. We exclude classification and recommendation sub-tasks relying on additional inference components. Dataset is released under a GNU General Public License v3.0 license.
CSFCUBE: The dataset consists of 50 queries labelled for relevance against about 120 candidates per query. Dataset is released under a Creative Commons Attribution-NonCommercial 4.0 International license.
C Co-citation Context Encoder
Here we present details of alternative design choices for our co-citation context encoder. In the use of  $\mathsf{BERT}_{\mathcal{E}}$  , we note in  $\S 3.2.2$  that this encoder is kept frozen during the course of training
<sup>5</sup>Released under a CC BY-NC 2.0. license.
$^{6}$ https://allenai.github.io/scispacy/
$\mathrm{BERT}_{\theta}$ . Fine-tuning  $\mathrm{BERT}_{\mathcal{E}}$  via a straight-through estimator (Bengio et al., 2013) under-performed freezing it. Using other encoders for scientific text such as SPECTER as  $\mathrm{BERT}_{\mathcal{E}}$  under-performed CoSentBert. A recent strong model for sentence representation MPNet-1B<sup>7</sup> lead to similar performance on abstract and aspect-conditional tasks as CoSentBert, indicating that a minimum requisite sentence encoder is all that is needed for  $\mathrm{BERT}_{\mathcal{E}}$ .
Here we provide further detail on the baselines overviewed in  $\S 4$
MPNET-1B & OTMPNET-1B: A sentence level baseline of a MPNet (Song et al., 2020) base model, fine-tuned on 1.17 billion similar text pairs in a contrastive learning setup. $^{8}$  This training data broadly represents web and scientific texts. Further we combine MPNET-1B with an OT based aggregation scheme similar to our multi-match model to yield, OTMPNET-1B a baseline using optimal transport with a performant sentence encoder.
SimCSE: A recent sentence representation model (Gao et al., 2021). We compare to two model variants: an unsupervised model UNSIMCSE-BERT, and a variant supervised with NLI data, SUSIMCSE-BERT.
Sentence-Bert: A sentence level transformer model fine-tuned on similar sentence pairs (Reimers and Gurevych, 2019). We compare performance to two variants, SENTBERT-PP and SENTBERT-NLI, fine-tuned on paraphrases and natural language inference (NLI) data respectively.
CoSentBert: The sentence-level model we describe in §3.2.2: A ScIBERT model finetuned on co-citation sentence contexts referencing the same set of co-cited papers.
ICTSENTBERT: A SCIBERT sentence model trained using the self-supervised inverse close task (Lee et al., 2019). Here we train abstract sentence representations to capture the semantics of their paragraph contexts.
SPECTER: A state of the art abstract level representation (Cohan et al., 2020). Here a SCiB-ERT model is fine-tuned to maximize similarity between representations of cited papers.
We also train a variant of this model on co-cited papers: SPECTER-COCITE.
SCINCL: A recent concurrent state of the art abstract level representation (Ostendorff et al., 2022). This approach trains a model similar to SPECTER, with improvements to the negative sampling strategies of Cohan et al. (2020) for contrastive learning. This presents a complementary contribution to the one presented in our work - with future modeling approaches likely to benefit from both approaches.
For the baselines described above specific model names from the Hugging Face and Sentence Transformers libraries are as follows:
MPNet-1B: HF; sentence-transformers/all-mpnet-base-v2.
SimCSE: HF; princeton-nlp/sup-simcse-bert-base-uncased, princeton-nlp/unsup-simcse-bert-base-uncased.
Sentence-Bert: ST; Paraphrases: paraphrase-TinyBERT-L6-v2. NLI: nli-roberta-base-v2 from the Sentence-Transformers library.
E Training
All our approaches are trained using the Adam optimizer with an initial linear warm-up for 2000 steps followed by a linear decay using gradient accumulation for a batch size of 30. The margin  $m$  in the triplet loss is set to 1. We implement all methods using PyTorch, HuggingFace, and GeomLoss libraries. Training convergence is established based on the loss on a held out set of co-citation data ensuring that training does not rely on a labelled dataset for convergence checks.
All experiments were run with data parallelism over servers nodes with the following GPU configurations:  $8 \times 12$ GB NVIDIA GeForce GTX 1080 Ti GPUs,  $4 \times 24$ GB NVIDIA Tesla M40 GPUs, or  $2 \times 48$ GB NVIDIA Quadro RTX 8000 GPUs. Servers had 12-24 CPUs per node and 256-385GB RAM. The training time per experiment varied from 5-20 hours, and the experiments in this paper represent about 4746 GPU hours of training.
F Model Hyper-Parameters
Here we report the best performing model hyperparameters. This is done per training dataset. For
7MPNet-1B: https://bit.ly/2Zbm2Iq
8MPNet-1B:https://bit.ly/2Zbm2Iq
10 https://www.sbert.net/docs/
pretrained_models.html
computer science trained models evaluated on CS-FCUBE:
- Specter-CoCiteScib: LR 2e-5.
- Specter-CoCiteSpec: LR 2e-5.
TSASPIRESpec: LR 2e-5.
OTASPIRESpec: LR 2e-5.  $\tau$  0.5.
- TS+OTASPIRESpec: LR 1e-5.  $\tau$  0.5.
For biomedical trained models evaluated on TRECCOVID and RELISH:
- Specter-CoCiteScib: LR 2e-5.
- Specter-CoCiteSpec: LR 2e-5.
TSASPIRESpec: LR 2e-5.
- OTASPIRESpec: LR 2e-5.  $\tau$  5000.
- TS+OTASPIRESpec: LR 2e-5.  $\tau$  5000.
For biomedical+computer science trained models evaluated on TRECCOVID and RELISH:
- Specter-CoCiteScib: LR 2e-5.
- Specter-CoCiteSpec: LR 2e-5.
TSASPIRESpec: LR 1e-5.
- OTASPIRESpec: LR 1e-5.  $\tau$  5000.
- TS+OTASPIRESpec: LR 1e-5.  $\tau$  5000.
We found it beneficial to use a low temperature  $\tau$  in computing distributions  $x$  for OT computation for CSFCUBE - a fine-grained similarity dataset. On the other hand we found it beneficial to use a high temperature  $\tau$  in computing distributions  $x$ , causing it to be effectively uniform, for OT computation in whole-abstract datasets SCIDOCS, RELISH, and TRECCOVIDRF. This is reasonable given the nature of similarity captured in these datasets. Hyper-parameters of the underlying encoders were not changed from their default values - other hyperparameters are common to methods and described in §4.
Finally, in computing OT transport plans, we optimize a entropy regularized objective:  $\min_{\mathbf{P}\in S}\langle \mathbf{D},\mathbf{P}\rangle -\frac{1}{\lambda} h(\mathbf{P})$  . Our experiments use a fixed value of  $\lambda = 20$
Hyper-parameter tuning: We tune the hyperparameters of all the ablated and proposed methods across the different datasets on development set performance. For CSFCUBE the Aggregated dev set performance was used for computer science training data models,  $\mathrm{TRECCOVID}_{\mathrm{RF}}$  and RELISH dev sets were used for biomedical data models with ties between the two broken by the more challenging  $\mathrm{TRECCOVID}_{\mathrm{RF}}$  performance, and computer science +biomedical data models were tuned on average
task performance of SciDOCS tasks. Given the expense of training models (about 20h for the proposed models) we first tune softmax temperatures then tuned learning rates. Large changes across learning rates weren't observed for the models. All learning rates are tuned over the range  $\{1\mathrm{e} - 5,2\mathrm{e} - 5,3\mathrm{e} - 5\}$ , OT sentence softmax temperatures  $\tau$  are tuned over  $\{0.5,1,5,5000\}$ , and softmax temperatures for ablation A3 was tuned over  $\{0.5,1,5\}$ .
G SciDOCS Benchmark Result
SciDocs Benchmark: Table 3 indicates performance on the abstract level document similarity benchmark SciDOCS of Cohan et al. (2020). First we note that the strong performance of SPECTER indicates a smaller gap to be closed. Here, although our proposed methods see similar performance to each other they consistently outperform SPECTER on 3 of 4 tasks establishing state of the art performance. Given SPECTER's citation training signal and our co-citation signal, we see better performance on the Citations and Co-Citation tasks respectively. Finally, note that our co-citation trained approaches broadly see better performance (1-1.5 points) on extrinsic tasks of Co-Reads and Co-Views indicating the value of this signal.
Here we ablate a range of model components in establishing factors which contribute performance. In ablations we only report performance on CS-FCUBE, TRECCOVIDRF, and RELISH.
A1. Does TSASPIRE gain from textual supervision over the encoder used to compute alignment? TSASPIRE relies upon a sentence alignment encoder, BERT $_{\mathcal{E}}$  in §3.2.2, to compute alignments,  $\hat{i_p}, \hat{i}_{p'}$ , from the co-citation context to the co-cited abstracts. Here we investigate if improvements in TSASPIRE are attributable to BERT $_{\mathcal{E}}$  or to the co-citation contexts themselves. We investigate this by comparing the performance of TSASPIRE to a model trained to maximize the alignment between abstract sentences directly computed using BERT $_{\mathcal{E}}$ , we refer to this as ABSASPIRE. This may be viewed as a form of knowledge distillation where alignments from a more local sentence encoder model, BERT $_{\mathcal{E}}$ , are distilled into the contextual sentence encoder of TSASPIRE. As Table 4 shows, TSASPIRE consistently outperforms ABSASPIRE, indicating the value added by natural language supervision from the co-citation contexts.
SciDocs tasks →
Models	Citations	Co-Citations	Co-Reads	Co-Views
MAP	NDCG	MAP	NDCG	MAP	NDCG	MAP	NDCG
MPNET-1B	86.76	92.63	85.68	92.16	83.45	90.47	82.51	89.29
SPECTER	92.39	95.90	88.32	93.88	86.42	92.39	84.65	90.70
SPECTER-COCITEScib	89.16 ±0.33	93.97 ±0.28	90.21 ±0.18	94.76 ±0.14	86.85 ±0.22	92.51 ±0.18	85.70 ±0.16	91.37 ±0.09
SPECTER-COCITESpec	89.85 ±0.10	94.26 ±0.08	90.82 ±0.17	95.11 ±0.11	87.14 ±0.14	92.65 ±0.13	85.81 ±0.10	91.35 ±0.05
TSASPIRESpec	90.99 ±0.26	95.04 ±0.17	90.92 ±0.06	95.26 ±0.05	87.51 ±0.07	92.97 ±0.06	85.87 ±0.20	91.46 ±0.14
OTASPIRESpec	91.13 ±0.28	95.08 ±0.20	90.88 ±0.13	95.25 ±0.02	87.50 ±0.14	92.90 ±0.12	85.70 ±0.20	91.30 ±0.11
TS+OTASPIRESpec	91.09 ±0.33	95.03 ±0.17	90.83 ±0.08	95.22 ±0.05	87.60 ±0.05	92.98 ±0.01	85.81 ±0.25	91.42 ±0.15