Tencent Technology (Shenzhen) Company Limited

Hang Kong

Hao Ye

This application discloses a summary generation using natural language processing via method, apparatus, and a computer device. The summary generation is implemented by obtaining a document, the document including at least one sentence. The summary generation is further implemented by extracting m candidate sentences from the document by using an extractive model. The summary generation is further implemented by outputting target words according to the m candidate sentences by using an abstractive model, and generating a summary according to the target words.

Summary generation method, apparatus, computer device, and storage medium

Disclosed are interest tag determining method and device for accurately promoting information for users in a group based on interest tags of the group. This includes obtaining a group identifier and a corresponding group name, obtaining user identifiers corresponding to the group identifier and first user contents of the user identifiers, concatenating the first user contents of the user identifiers corresponding to the group identifier to obtain a first group content of the group identifier, calculating probabilities that the first group content is under the topic identifiers, and determining interest tags of the group identifier according to the probabilities that the first group content is under the topic identifiers and an association relationship between the topic identifiers and the interest tags that is determined through pre-training.

Interest tag determining method, computer device, and storage medium

An information processing method includes obtaining text information and a sentence set; encoding a sentence in the sentence set using a first encoder to obtain a first encoded vector, and encoding the sentence using a second encoder to obtain a second encoded vector. The first encoded vector is determined according to the sentence, and the second encoded vector is determined according to a feature of the sentence. The method also includes determining a sentence encoded vector according to the first and second encoded vectors; encoding the sentence encoded vector using a third encoder to obtain global information; decoding the global information using a decoder; and determining a probability value corresponding to the sentence. Accordingly, when a deep learning method is used, a manually extracted sentence is further added to perform feature training, to effectively improve a learning capability of a model, thereby improving an information processing capability and effect.

Growing community of inventors

Guangdong, China

Hang Kong