* If you want to update the article please login/register
Text semantic embedding is achieved by text semantic embedding based on the content of the source corpus' content to produce summaries, assisting users in extracting valid information from huge amounts of text data. Abstract summarization is the process of abstract summarization. The existing model's poor ability to detect sentence formulation's correctness have resulted in the production of summaries with distorted factual content and pictures that do not match the text's equivalent information, i. e. To improve the model's ability to understand facts, implicit reasoning is embedded into the encoder model of the summary by parameter sharing. This paper first uses the extraction software Lead-3 to extract partial sentences from the original text as template sentences in order to improve the factual consistency of the generated content. The text-implication task is jointly prepared with the text-summarization task to make the encoder text-implication aware by sharing the same BERT-based text encoder.
Source link: https://europepmc.org/article/PPR/PPR564574
* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions