Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Accuracy | 1 |
| Algorithms | 1 |
| Artificial Intelligence | 1 |
| Computer Assisted Testing | 1 |
| Computer Software | 1 |
| Models | 1 |
| Natural Language Processing | 1 |
| Reading Comprehension | 1 |
| Reading Strategies | 1 |
| Scoring | 1 |
| Semantics | 1 |
| More ▼ | |
Source
| Grantee Submission | 1 |
Author
| Allen, Laura K. | 1 |
| Botarleanu, Robert-Mihai | 1 |
| Crossley, Scott Andrew | 1 |
| Dascalu, Mihai | 1 |
| McNamara, Danielle S. | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2021
Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. This study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale.…
Descriptors: Computer Assisted Testing, Scoring, Natural Language Processing, Computer Software

Peer reviewed
Direct link
