CitationAS: A Tool of Automatic Survey Generation Based on Citation Content
, , et
22 juin 2018
À propos de cet article
Catégorie d'article: Research Paper
Publié en ligne: 22 juin 2018
Pages: 20 - 37
Reçu: 17 déc. 2017
Accepté: 08 mai 2018
DOI: https://doi.org/10.2478/jdis-2018-0007
Mots clés
© 2018 Walter de Gruyter GmbH, Berlin/Boston, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Figure 1
Figure 2
Figure 3
Citation Sentences Examples_
| No. | Citation sentence |
|---|---|
| 1 | Their transcription is dependent on mouse Cebpe and human CEBPE [12]. |
| 2 | These changes may derive in a higher risk for type 2 diabetes development [8], [9]. |
| 3 | It interacts with a variety of transcriptional factors and MLL proteins [9]–[12]. |
| 4 | Most pathogens of humans, animals and plants are multi-host pathogens [1]–[3], [20]. |
Six Methods Rankings based on Two Volunteers when Considering Sentence Location_
| Ranking | Volunteer A | Volunteer B |
|---|---|---|
| 1 | Lingo-MMR | Lingo-MMR |
| 2 | Lingo-TF-IDF | Lingo-TF-IDF |
| 3 | STC-TF-IDF | STC-MMR |
| 4 | STC-MMR | STC-TF-IDF |
| 5 | bisecting K-means-TF-IDF | bisecting K-means-MMR |
| 6 | bisecting K-means-MMR | bisecting K-means-TF-IDF |
Six Methods Rankings based on Two Volunteers_
| Ranking | Volunteer A | Volunteer B |
|---|---|---|
| 1 | STC-TF-IDF | Lingo-TF-IDF |
| 2 | Lingo-TF-IDF | Lingo-MMR |
| 3 | STC-MMR | STC-MMR |
| 4 | Lingo-MMR | STC-TF-IDF |
| 5 | bisecting K-means-MMR | bisecting K-means-MMR |
| 6 | bisecting K-means-TF-IDF | bisecting K-means-TF-IDF |
Top 20 Phrases According to High Frequency_
| Phrase (Frequency) | Phrase (Frequency) |
|---|---|
| cell line (37507) | reactive oxygen species (5160) |
| gene expression (37001) | central nervous system (4418) |
| amino acid (35165) | smooth muscle cell (3439) |
| transcription factor (25626) | protein protein interaction (3286) |
| cancer cell (25605) | single nucleotide polymorphism (2535) |
| stem cell (22567) | tumor necrosis factor (2482) |
| growth factor (17531) | genome wide association (2386) |
| signaling pathway (16597) | case control study (2269) |
| cell proliferation (14203) | false discovery rate (2209) |
| meta analysis (12647) | innate immune response (2133) |
Topic Distribution in Dataset_
| Topic No. | Topic words |
|---|---|
| 1 | |
| 2 | disease, |
| 3 | |
| 4 | study, health, patient, year, |
| 5 | |
| 6 | |
| 7 | |
| 8 | acid, increase, level, activity, |
| 9 | study, process, task, response, visual, effect, memory, information, social, related |
| 10 | cell, signalling, pathway, |
Evaluation Standards_
| Score | Evaluation standards |
|---|---|
| 5 | Sentences are very smooth. Paragraphs and surveys are very comprehensive, exist very small redundancy and can fully reflect retrieval topics. The logical structure of survey is reasonable. |
| 4 | Sentences are relatively smooth. Paragraphs and surveys are relatively comprehensive, exist relatively small redundancy and can relatively reflect retrieval topics. The logical structure of survey is relatively reasonable. |
| 3 | Sentences are basically smooth. Paragraphs and surveys are basically comprehensive, exist certain redundancy and can basically reflect retrieval topics. The logical structure of survey is basically reasonable. |
| 2 | Sentences are not smooth enough. Paragraphs and surveys are not comprehensive, exist relatively high redundancy and cannot reflect retrieval topics enough. The logical structure of survey is confusing. |
| 1 | The smoothness of sentences becomes very poor. Paragraphs and surveys are far from comprehensive, exist very high redundancy and cannot fully reflect retrieval topics. There is no logical structure in the survey. |