Research On Answer Summarization Of Virtual Q&A Community Based On Improved Harris Hawk Optimization Algorithm

In the era of Web2.0,people have become the producers of Internet content.People are willing to ask and answer questions on the virtual Q&A community platform.Therefore,these Q&A communities have gradually become a platform for people to create,share and transfer knowledge.With the influx of a large number of users,as well as the precipitation and accumulation of knowledge resources,it is difficult for users to effectively and quickly obtain the information they want in the mass of Q&A.How to help community users to efficiently obtain useful knowledge information has become a research hotspot in recent years.Answer summarization is an effective solution,which can solve the problem that the single answer content is one-sided when different users answer the same question because of their own knowledge constraints and subjectivity.Answer summarization can summarize the answer set of the same question from multiple perspectives,and provide a comprehensive and high-quality document summary,so as to improve the user experience and realize the reuse of community knowledge resources.In order to help users acquire knowledge resources efficiently,this paper studies the extraction of answer summarization for open-ended questions.Because there is no fixed answer to the open-ended question,users want to answer this question comprehensively from multiple perspectives when they search for knowledge.At the same time,considering the quality,repetition and readability of the summarization content,this paper transforms the process of summarization extraction into a multi-objective optimization problem.Taking coverage,redundancy,consistency,coherence and logicality as objective functions and answer summarization length as constraints,a multi-objective optimization model is constructed.In this paper,the Harris hawk optimization algorithm(HHO)is improved,and a method based on the binary multi-objective Harris hawk optimization algorithm(BMHHO)is proposed to extract the answer summarization,so as to solve the multi-objective optimization model.In this paper,we get the data from the "Zhihu" Q&A community,and compare BMHHO algorithm with other summarization algorithms and multi-objective optimization algorithms to verify whether the method proposed in this paper is better in the application of answer summarization extraction.