ArticleJournal of environmental and public health2022
Automatic Text Summarization for Public Health WeChat Official Accounts Platform Base on Improved TextRank.
Article in Journal of environmental and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Women's Preferences and Willingness to Pay for AI Chatbots in Women's Health: Discrete Choice Experiment Study.Journal of medical Internet research · 2025Article
- Retracted: Automatic Text Summarization for Public Health WeChat Official Accounts Platform Base on Improved TextRank.Journal of environmental and public health · 2023Article
Corrections and comments
- Retraction · 2023-08-23Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of we-media information dissemination, WeChat official accounts platform has become an important way for people to obtain health related knowledge. However, the platform information is redundant, miscellaneous, and overloaded. In order to meet the increasingly accurate and efficient knowledge service needs of users, reorganizing and aggregating document knowledge resources is effective. If we use the way of artificial recognition to filter information, it will inevitably cause huge labor and time cost, and the effect is very little in front of massive articles. This paper proposes a text summarization method for the WeChat platform based on improved TextRank that takes into account both user demands and sentence features during the summarization process. The data source crawled from the Sogou WeChat platform. The results show that the TextRank algorithm has obvious hints on the accuracy of text summarization extraction after fusing the Word2vec model. The improved TextRank method, integrating user demands and sentence features into the model, makes the results of text summarization closer to the theme of the article and more able to meet the user demand. According to the complexity of the algorithm, this method is not suitable for the automatic summarization of long or multiple documents.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.