言語処理学会 第30回年次大会 発表論文集（2024年3月）

Large Language Models as Generalizable Text-to-Table Systems
Steven Coyne1∗ Yuyang Dong2
1 Tohoku University
2 NEC Corporation
coyne.steven.charles.q2@dc.tohoku.ac.jp
dongyuyang@nec.com

Abstract
Wu et al. [1] formalized the task of text-to-table generation, in which unstructured texts are summarized in table form. However, their experiments focus on ﬁne-tuned,dataset-speciﬁc models that show limited performance outside of the data they were trained on. As the task assumes
no user query, systems ideally generate valid tables acrossvarious input texts without favoring the layout or tablecount of a particular dataset. To address this, we apply
prompted large language models (LLMs) to this task, assessing their suitability as generalizable text-to-table systems. To more accurately rate outputs, particularly in azero-shot setting, we revise the evaluation script from [1]to align tables by column similarity before scoring.

1

Figure 1

Example of an input text and reference table in the
text-to-table task. The table format is adapted from [1], with their<NEWLINE> separator replaced with line breaks for readability.

the table design process include the following:
Entity Linking: An entity may be referred to in multiple ways due to nicknames, metonymy, and otherphenomena. A basketball team may be referred to by
its name or by its home city. There may be a switch
between these partway through the text. The model
must resolve these references.
Implicit Reasoning: Some information in a text may
be implied by other facts. If a basketball game iswon 120-90, and the winning team is named, then themodel must assign 90 to the losing team even if nosentence explicitly describes this.
Contextual Understanding: Depending on the input text, the “best” way to represent information may
diﬀer signiﬁcantly. In a biographical description, information may be best summarized as a one-row tablein which other entities are listed only in relation to themain entity the text is focused on.

Introduction

Text-to-table generation is a sequence-to-sequence task
in which one or more structured tables are generated basedon the content of an input text [1]. The input can be asentence or a longer document. The output is a table that
attempts to represent the information in the input text. An
example using a basketball game report from the RotoWire
dataset [2] can be seen in Figure 1.
In this task, there is no explicit user query. Tables aredesigned based only on the input text. This means thatthe task is at least partially open-ended, as there may be
multiple valid tables or sets of tables that could accurately
represent an input text. As a result, the “ground truth”
reference tables in the datasets are not deﬁnitive.
As the goal of the text-to-table setting is to convert an
input text into a “valid” table that makes sense with respect
to the input, it is a high-level logical task that may depend
on world knowledge and common sense reasoning. Examples of the sort of inferences that may be done as part of∗

The work was done when Steven Coyne was an intern at NEC.

Such principles may be learned as part of a ﬁne-tuning
process with respect to a particular dataset of relatively
similar texts and tables. This can be seen in previous works
such as [1] and [3]. However, these models are limited tothe speciﬁc datasets that they were trained on and may― 3243 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

show poor performance on tables that do not resemble thethree used in their study. To unify handling of all datasets,

training data’s format or use similar header names.

and hypothesizing that a more common structured format

Given their strong performance across a number of rea-

may be easier for LLMs to generate consistently, we use asoning tasks [4], we hypothesize that LLMs such as GPT-3

JSON format for our experiments. Furthermore, a JSON[5] can perform this task in a generalizable manner with-format permits experimentation with JSON-speciﬁc output

out the need to ﬁne-tune on hundreds or thousands of ex-

parameters in our selected LLMs.

amples, taking advantage of principles such as chain-ofthought reasoning [6] or in-context learning [5] to address

3.3

Evaluation

the aforementioned reasoning challenges. To this end, we

For evaluation, we use the scorer released as part of [1].

experiment with LLM prompting strategies using several

This scorer parses tables into relations and scores them

diﬀerent task decomposition settings, which we apply uni-with three metrics: exact match, chrF [13], and BERTScore

formly to each of the text-to-table datasets designated in [1][14]. We modify this scoring script to handle JSON inputs

to test the generalized performance of the selected models.

as part of the format change. We also create a JSON conver-2

sion script to allow the baseline system outputs reproduced
from [1] to be evaluated by the adapted scorer.

Related Work
Previous works have examined the use of LLMs to han-In addition to format adaptations, we further modify thedle table-based tasks. Li et al. [7] apply base and ﬁne-tuned

scoring script to allow tables in a multiple-table setting

GPT-3.5 models to diverse table tasks, showing strong per-to be aligned by similarity. The original script can only

formance and generalizability to new data and tasks. How-

match such tables by name exactly, but this is unlikely in

ever, their study does not include the text-to-table task

cases such as a zero-shot table generation, as a table namedescribed in [1]. Tang et al. [8] analyze the text-to-tablediﬀerence as small as “Team” and “team” would result in asetting using a variety of prompted LLMs, generating ta-score of zero. We implement a table-matching algorithm inbles in raw text, LATEX, and HTML. Their work focuses

which the candidate tables are aligned by the similarity ofon the challenges of evaluation in this task, rather than thetheir column names. We use the highest average chrF met-

generalizability of LLMs compared to seq2seq baselines

ric across column names to align tables. We experimented

across datasets. Our work also diﬀers in using a JSONwith using BERTScore for this purpose, but the resulting

format and in its approach to subtask decomposition.

scores were slightly lower. We hypothesize that cases like3

Methodology“Team” vs. “Teams,” in which there is some surface similarity, outnumber cases such as “Player” and “Athlete” in

3.1

Baseline and Datasets

which there are two distinct semantically similar words.

As a baseline, we use a system in [1], a seq2seq model

3.4

Our Method

based on BART [9]. We use the same datasets as their

We selected the OpenAI LLMs gpt-3.5-turbo-1106

work: E2E [10], WikiTableText [11], WikiBio [12], andand gpt-4-1106-preview for our experiments. Since the

RotoWire [2]. As WikiBio is large, we use a 10% subset

outputs of our prompts are in a JSON format, we include

in our experiments to control costs. We reproduce the ex-experiments with the response format parameter, per-periments in [1] based on their code,1）utilizing the “table

forming the gpt-3.5-turbo-1106 experiments in bothconstraint” and table relation embedding features. How-

text mode and JSON mode to compare them.

ever, to align with our other experiments, we modify theeration, resulting in the following subtasks. Examples of

output format and scoring script as described below.

3.2

We experiment with decomposing the task of table genthe outputs of each can be seen in the Appendix.

Output Format

Grouping: Entities in the text are listed and sorted
The system described in [1] uses an idiosyncratic format
that diﬀers between the RotoWire dataset and the otherinto categories. The input is the text, and the output
is one or more lists of entities wrapped in JSON.
Schema Generation: Based on the input text, and

1） Available at https://github.com/shirley-wu/text to table― 3244 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Figure 2

Prompt settings in our experiments. Experiments are performed with both few-shot and zero-shot prompts.

optionally the output of a grouping step, the model

were limited to the RotoWire dataset using the schema +generates a list of one or more JSON table schemas.

table and group + schema + table settings, using only JSON

Each schema is associated with a planned output table.
Table Generation: Based on the input text and one ormode. We additionally limit few-shot experiments to one
example to control costs. We selected RotoWire due tomore input schemas, potentially the result of a schema

its small size and high complexity, being the only dataset

generation step, the model generates tabular JSON.

with two-dimensional tables and multiple tables per input.

Each table row is represented by a JSON object, andWe selected JSON mode after it showed slightly improved

all rows in a given table use identical ﬁelds. Multiple

performance on RotoWire in the GPT-3.5 experiments.

tables are nested within a top-level JSON object.

5

Results and Discussion

We used a prompt chaining approach in which each ofthese subtasks was handled by a separate call to the LLM.

Results can be seen in Tables 1 and 2. In the “goldWe deﬁned both zero-shot and few-shot prompts for eachschema” few-shot setting, the scores of the LLMs were

step. The zero-shot prompts incorporated chain-of-thought

comparable or even superior to those of the respective[6]. In the few-shot prompts, example inputs and outputs

baseline ﬁne-tuned model. However, it must be stressed

were drawn randomly from the validation set and appended

that this setting involves a leak of ground-truth information.

before the inputs. We used 5-shot prompting for all datasets

In settings with generated schemas, the LLM scoresin the GPT-3.5 experiments, plus additional 1-shot prompting for RotoWire. We created two sets of prompts for the

are lower than the baseline model scores. However, the
LLMs achieve moderate performance across all datasets,

schema generation step: one using grouping step outputs

suggesting their capabilities are generalizable.
The LLMs display higher performance in few-shot set-and one using only the input text.

4tings compared to zero-shot, and in the case of RotoWire,

Experiments

higher performance at a higher number of examples.

Our ﬁrst experiments focus on the table generation step.

The results for GPT-3.5 show relatively similar scores

To test the performance of the LLMs in this subtask

between the JSON and text output formats. Which format

alone, we establish a setting where the ground-truth “goldscores higher varies by dataset, with E2E showing higher

schema” is extracted from the reference ﬁle and provided

scores in text mode and RotoWire showing higher scores inalong with the input text. Theoretically, this establishes an

JSON mode. All diﬀerences are under two points, except

upper limit of performance for other pipelines. Following

for the gold schema zero-shot E2E exact match score.

this, we performed pipeline experiments using generated

Overall, the experiments that included a grouping step

schemas, both with and without grouping. This resulted in

produced lower scores than the experiments using only
schema and table steps. In RotoWire, the group + schema

six total settings: zero-shot and few-shot versions of eachof the three prompt settings seen in Figure 2.
Due to budgetary constraints, our GPT-4 experiments+ table prompt chain performed better than the no-groupchain in a few-shot setting, and the no-group chain per-― 3245 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

System/Prompts

Datasetoutput format: text
Header F1
Non-Header F1
EM
Ch
BS
EM
Ch
BS

Baseline(Wu et al. 2022)

E2E
WikiBio (subset)
WikiTableText
RotoWire

99.63
78.70
76.14
88.89

99.70
83.37
81.97
91.56

99.88
91.98
93.13
93.14

97.88
66.83
57.34
85.03

98.00
75.25
66.95
87.47

98.56
74.63
78.18
92.97

99.63
78.70
76.14
88.89

99.70
83.37
81.97
91.56

99.88
91.98
93.13
93.14

97.88
66.83
57.34
85.03

98.00
75.25
66.95
87.47

98.56
74.63
78.18
92.97

gpt-3.5-turbo-1106
zero-shotgold schema + table

E2E
WikiBio (subset)
WikiTableText
RotoWire

100
99.17
99.78
99.86

100
99.35
99.81
99.86

100
99.63
99.93
99.86

18.46
46.29
46.77
75.61

56.28
71.49
59.69
83.74

69.56
66.25
72.29
93.76

100
99.27
99.78
100

100
99.46
99.81
100

100
99.78
99.96
100

18.54
46.18
46.81
75.35

56.37
71.53
59.69
83.62

69.55
66.22
72.34
93.78

E2E
WikiBio (subset)
WikiTableText
RotoWire (1-shot)
RotoWire (5-shot)

100
99.70
99.76
99.49
100

100
99.70
99.79
99.50
100

100
99.71
99.94
99.50
100

72.63
62.95
59.03
85.85
88.32

84.25
80.36
69.63
88.73
90.37

88.45
77.49
78.99
97.23
98.37

100
99.96
99.89
99.12
100

100
99.97
99.90
99.13
100

100
99.97
99.98
99.15
100

69.01
62.69
58.24
85.54
88.09

83.04
80.23
69.02
88.32
90.19

87.11
77.38
78.38
96.92
98.32

E2E
WikiBio (subset)
WikiTableText
RotoWire

21.36
0.00
0.00
23.57

42.11
36.56
27.11
49.61

77.46
58.38
69.66
55.10

2.38
0.00
0.00
16.40

18.08
24.02
18.78
40.93

41.87
28.57
42.51
44.19

21.27
0.00
0.00
23.67

42.04
36.70
26.94
49.31

77.42
58.54
69.72
55.19

2.40
0.00
0.00
16.60

18.09
23.94
18.48
40.79

41.94
28.40
42.79
43.96

E2E
WikiBio (subset)
WikiTableText
RotoWire (1-shot)
RotoWire (5-shot)

91.70
52.72
45.79
52.70
73.63

93.42
64.69
58.67
64.69
79.96

98.89
82.42
90.28
67.98
83.65

70.23
37.14
29.68
51.47
73.29

80.12
50.81
41.17
60.96
77.21

86.27
50.07
65.11
67.53
84.94

91.65
52.73
46.05
53.84
73.56

93.40
64.66
58.74
65.67
80.02

98.73
82.45
90.16
68.60
83.90

68.47
37.01
29.85
52.79
73.21

79.90
50.72
41.23
61.95
77.18

85.48
49.94
64.99
68.53
85.18

E2E
WikiBio (subset)
WikiTableText
RotoWire

23.84
0.00
0.14
11.16

41.95
38.02
21.76
43.69

74.00
58.98
75.14
39.75

1.83
0.00
0.08
8.43

16.45
23.75
12.07
35.03

30.86
26.70
32.78
28.05

23.79
0.00
0.13
10.95

42.04
38.79
21.51
43.03

74.11
60.24
74.35
39.25

1.93
0.00
0.10
7.97

16.65
24.04
11.77
34.10

31.23
26.87
32.41
27.47

E2E
WikiBio (subset)
WikiTableText
RotoWire (1-shot)
RotoWire (5-shot)

90.20
54.42
43.48
61.29
73.47

92.21
65.58
55.69
72.04
80.57

98.52
82.72
90.02
76.11
84.03

68.44
35.61
28.45
60.53
73.23

78.50
48.93
39.23
68.84
77.71

85.43
48.16
64.20
76.62
85.38

90.20
54.71
42.86
61.51
73.55

92.20
65.86
55.50
72.33
80.59

98.38
83.20
89.95
76.18
84.04

66.76
35.54
28.18
60.53
73.11

78.38
49.12
39.03
68.92
77.64

84.61
48.16
64.04
76.84
85.42

gpt-3.5-turbo-1106
few-shotgold schema + tablegpt-3.5-turbo-1106
zero-shot
schema + tablegpt-3.5-turbo-1106
few-shot
schema + tablegpt-3.5-turbo-1106
zero-shotgroup + schema + tablegpt-3.5-turbo-1106
few-shotgroup + schema + table

output format: json
Header F1
Non-Header F1
EM
Ch
BS
EM
Ch
BS

Table 1 Scores for gpt-3.5-turbo-1106. E, Ch, and BS refer to exact match, chrF, and BERTScore, respectively. output format
does not apply to baseline scores, so scores are simply repeated. Scores in bold are higher than the baseline, but it should be noted thatthe gold schema setting involves a leak of reference table information. Scores for RotoWire are the average of Team and Player scores.
Header F1
Ch
BS

Non-Header F1
EM
Ch
BS

6

Conclusion

System/Prompts

EM

Baseline

88.89

91.56

93.14

85.03

87.47

92.97

The experiments suggest that our prompt settings canzero-shot S+T

23.59

53.11

52.70

19.38

43.91

44.14

permit LLMs such as GPT-3.5 and GPT-4 to serve as

one-shot S+T

43.35

64.33

66.00

42.26

60.23

63.63

relatively generalizable text-to-table generation systems.

zero-shot G+S+T

16.40

47.85

50.88

13.50

38.38

42.35

While their performance lagged behind the baseline ﬁne-one-shot G+S+T

54.67

71.03

73.92

52.16

66.40

71.84

tuned systems in general, the LLMs show promise in thisScores for gpt-4-1106-preview on the RotoWire
dataset, using the average of Team and Player scores.

task, particularly in few-shot settings in which some basic

Table 2

formed better in zero-shot. These trends were seen in both
gpt-3.5-turbo-1106 and gpt-4-1106-preview.
Interestingly, while GPT-4 outperformed GPT-3.5 in thezero-shot setting, the reverse was seen in the one-shot RotoWire experiments, where GPT-3.5 scored higher.
Finally, the models were able to reliably generate valid
JSON in all experiments, even without using JSON mode.

information about the target table format can be provided.
Additionally, the models’ high performance in the gold
schema experiments suggests that they may be able to follow user-deﬁned schemas reliably. There remain many
unexplored possibilities for prompting settings in this task,
and our decomposition experiments are by no means exhaustive. Future works may explore additional approaches
to prompting, format decisions, and table evaluation.

― 3246 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Acknowledgments
This work is mainly supported by NEC Corporationand partially supported by JSPS KAKENHI Grant Number
JP22H00524.

References[1] Xueqing Wu, Jiacheng Zhang, and Hang Li. Text-to-table:
A new way of information extraction. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long
Papers), pp. 2518–2533, Dublin, Ireland, May 2022. As-sociation for Computational Linguistics.
[2] Sam Wiseman, Stuart Shieber, and Alexander Rush. Challenges in data-to-document generation. In Martha Palmer,
Rebecca Hwa, and Sebastian Riedel, editors, Proceedings of the 2017 Conference on Empirical Methods in
Natural Language Processing, pp. 2253–2263, Copen-

hagen, Denmark, September 2017. Association for Computational Linguistics.
[3] Tong Li, Zhihao Wang, Liangying Shao, Xuling Zheng, Xiaoli Wang, and Jinsong Su. A sequence-to-sequence&set
model for text-to-table generation. In Anna Rogers, Jordan
Boyd-Graber, and Naoaki Okazaki, editors, Findings of
the Association for Computational Linguistics: ACL
2023, pp. 5358–5370, Toronto, Canada, July 2023. Asso-

ciation for Computational Linguistics.
[4] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei
Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie
Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen,
Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu
Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong
Wen. A survey of large language models. arXiv preprint
arXiv:2303.18223, 2023.
[5] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell,
Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger,
Tom Henighan, Rewon Child, Aditya Ramesh, Daniel
Ziegler, Jeﬀrey Wu, Clemens Winter, Chris Hesse, MarkChen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin
Chess, Jack Clark, Christopher Berner, Sam McCandlish,
Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In H. Larochelle,
M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing
Systems, Vol. 33, pp. 1877–1901. Curran Associates,
Inc., 2020.
[6] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten
Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny
Zhou. Chain-of-thought prompting elicits reasoning inlarge language models, 2023.
[7] Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge,
Haidong Zhang, Danielle Riﬁnski Fainman, Dongmei
Zhang, and Surajit Chaudhuri. Table-gpt: Table-tuned
gpt for diverse table tasks, 2023.

[8] Xiangru Tang, Yiming Zong, Jason Phang, Yilun Zhao,
Wangchunshu Zhou, Arman Cohan, and Mark Gerstein.
Struc-bench: Are large language models really good at
generating complex structured data?, 2023.
[9] Mike Lewis, Yinhan Liu, Naman Goyal, Marjan
Ghazvininejad, Abdelrahman Mohamed, Omer Levy,
Veselin Stoyanov, and Luke Zettlemoyer. BART: Denoising sequence-to-sequence pre-training for natural language
generation, translation, and comprehension. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th Annual Meeting of the
Association for Computational Linguistics, pp. 7871–
7880, Online, July 2020. Association for Computational
Linguistics.
[10] Jekaterina Novikova, Ondřej Dušek, and Verena Rieser.
The E2E dataset: New challenges for end-to-end generation. In Kristiina Jokinen, Manfred Stede, David DeVault,
and Annie Louis, editors, Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pp.
201–206, Saarbrücken, Germany, August 2017. Association for Computational Linguistics.
[11] Junwei Bao, Duyu Tang, Nan Duan, Zhao Yan, Yuanhua Lv, Ming Zhou, and Tiejun Zhao. Table-to-text:
Describing table region with natural language. Proceedings of the AAAI Conference on Artiﬁcial Intelligence, Vol. 32, No. 1, Apr. 2018.

[12] Rémi Lebret, David Grangier, and Michael Auli. Neuraltext generation from structured data with application to the
biography domain. In Jian Su, Kevin Duh, and Xavier Carreras, editors, Proceedings of the 2016 Conference on
Empirical Methods in Natural Language Processing,pp. 1203–1213, Austin, Texas, November 2016. Association for Computational Linguistics.
[13] Maja Popović. chrF: character n-gram F-score for automatic MT evaluation. In Ondřej Bojar, Rajan Chatterjee, Christian Federmann, Barry Haddow, Chris Hokamp,
Matthias Huck, Varvara Logacheva, and Pavel Pecina, editors, Proceedings of the Tenth Workshop on Statistical Machine Translation, pp. 392–395, Lisbon, Portugal,
September 2015. Association for Computational Linguistics.
[14] Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q.
Weinberger, and Yoav Artzi. Bertscore: Evaluating text
generation with bert. In International Conference on
Learning Representations, 2020.

― 3247 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

A" type ":" integer "}," Field goals attempted ":{" type ":" integer "}," Field goals made ":{" type ":" integer "}," Points ":{" type ":" integer "}," Total rebounds ":{" type ":" integer "}," Steals ":{" type ":" integer "}AppendixHere, we present an example of the JSON format ofour prompt chains, extending the example from Figure 1.
This particular example is converted from the RotoWire
development set, not generated by one of our models.
Input Text
The Wizards launched yet another comeback on Tuesday, thistime feasting on the relatively inexperienced Los Angeles
Lakers. Washington not only overcame a 13 - point fourth
quarter deficit, but won by double digits as well. The
team shot over 51 percent from the field on the night andoutscored LA 37 - 13 in the fourth. At the crux of the win
was All-Star point guard John Wall, who scored 34 points
to go along with 14 assists and four steals. Wall was an
excellent 14 - of - 25 from the field. LA, meanwhile, saw
promising play from its own point guard. Sophomore D’Angelo
Russell filled the stat sheet respectably, shooting 10 of - 21 for 28 points to go along with nine assists andsix rebounds. His backcourt partner, Jordan Clarkson, added
another 22 points and shot 10 - of - 19.

}}]

Table Output Format{" Team ":[{" Team ":" Lakers " ," Percentage of field goals ":51 ," Points in 4 th quarter ":13},{
" Team ":" Wizards " ," Percentage of field goals ": null ," Points in 4 th quarter ":37}],
" Player ":[{" Player ":" John Wall " ," Assists ":14 ," Field goals attempted ":25 ," Field goals made ":14 ," Points ":34 ," Total rebounds ": null ," Steals ":4},{" Player ":" D ' Angelo Russell " ," Assists ":9 ," Field goals attempted ":21 ," Field goals made ":10 ," Points ":28 ," Total rebounds ":6 ," Steals ": null},{
" Player ":" Jordan Clarkson " ," Assists ": null ," Field goals attempted ":19 ," Field goals made ":10 ," Points ":22 ," Total rebounds ": null ," Steals ": null}]Grouping Output Format{" Team ":[" Lakers " ," Wizards "]," Player ":[" John Wall " ,"D ' Angelo Russell " ," Jordan Clarkson "]}Schema Output Format[{" title ":" Team " ," type ":" object " ," properties ":{" Team ":{" type ":" string "}," Percentage of field goals ":{" type ":" integer "}," Points in 4 th quarter ":{" type ":" integer "}}},{" title ":" Player " ," type ":" object " ," properties ":{" Player ":{" type ":" string "}," Assists ":{}― 3248 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).