Summarization in English as a Foreign Language: A Study Comparing Summary Performances to Summarizers’ Vocabulary Size
Bibliographic record
Abstract
The study examined the impact of a first language’s summarizing skill and second language vocabulary size on summary performances in a second language. A total of 40 English as a Foreign Language (EFL) learners from a Japanese university with a mixed level of English language proficiency were asked to write a summary in English (i.e., their non-native language, L2) and in Japanese (their native language, L1) from a text written English and Japanese respectively. The effect of L1 summarizing skill on L2 summary performances was examined using multiple regression analysis. L1 summary performances (i.e., summarizing skill) slightly influenced English summary performances for summary writers with lower-level English language proficiency but not L2 summary performances for those with higher-level English language proficiency. The participants’ vocabulary size measured by Nation’s (2007) test was positively correlated with their English summary performances. Moreover, the results showed that the vocabulary size in the highest and smallest-vocabulary size groups was correlated with scores on two rating scales (i.e., Language use and Source use) in their English summary. In contrast, the vocabulary size in the middle-level vocabulary size groups was correlated with their scores on two different rating scales (i.e., Main idea coverage and Integration) in their English summary. This study concluded that L1 summary performance had not impact on L2 summary performances because several characteristics influence of summary writers’ English vocabulary size. The study made several recommendations to EFL teachers who teach summary writing and for further study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".