Over the years customers' expectation of getting information instantaneously
has given rise to the increased usage of channels like virtual assistants.
Typically, customers try to get their questions answered by low-touch channels
like search and virtual assistant first, before getting in touch with a live
chat agent or the phone representative. Higher usage of these low-touch systems
is a win-win for both customers and the organization since it enables
organizations to attain a low cost of service while customers get served
without delay. In this paper, we propose a two-part framework where the first
part describes methods to combine the information from different interaction
channels like call, search, and chat. We do this by summarizing (using a
stacked Bi-LSTM network) the high-touch interaction channel data such as call
and chat into short searchquery like customer intents and then creating an
organically grown intent taxonomy from interaction data (using Hierarchical
Agglomerative Clustering). The second part of the framework focuses on
extracting customer questions by analyzing interaction data sources. It
calculates similarity scores using TF-IDF and BERT(Devlin et al., 2019). It
also maps these identified questions to the output of the first part of the
framework using syntactic and semantic similarity.

本文提出了一个通过摘要网络和层次聚类技术来提取客户提问的框架，同时对客户的问题进行语法和语义相似度分析，并采用 TF-IDF 和 BERT 计算相似性得分。