Expedite auditing processes with Text Summarization powered by Artificial Intelligence mechanisms

Artificial Intelligence has a vast scope of utility to expedite processes and augment human productivity. Auditing business processes in a heavy document-laden environment is just one of them. Auditing is the cornerstone of quality-driven enterprises, where they have to frequently scour through paper logs and documents generated at the end of each intermittent process. Sampling as a part of the audit tool leaves scope for error. However, using Artificial Intelligence (AI), more specifically Deep Learning enabled process automation in the auditing realm allows to audit almost the entire universal data set.

How do AI and Deep Learning benefit auditing?

AI or Deep Learning allows generating text summaries in the most integrated form. As data explodes and generates high amounts of free-text data, auditing in the traditional sense seems next to impossible. AI / Deep Learning enabled mechanisms to allow to effectively curate and summarize data. Deep Learning uses Advanced Natural Language Processing (NLP) and Deep Neural Networks (DNN) to generate a new summarized logical sequence of words and sentences without changing the meaning of the text. The summarized data is tagged to the digitized asset as metadata that not only allows seamless auditing but also seamless storage, search, and retrieval. Deep Learning follows an Extractive Approach or an Abstractive Approach to generate the text summaries.

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What are Extractive and Abstractive Approaches in Deep Learning powered Text Summarization?

Extractive Summarization copies parts of sentences from the source through measured weights of importance and subsequently combines them to form a summary.

Abstractive Summarization generates new phrases by first understanding the text and then rephrasing words in the source in a condensed format. It is the toughest among the two approaches.

Abstractive Approach proves to be superior of the two

Extractive Approach though an old technique in the field of auto-summarization is not summarization per se. When human beings summarize content, they read the content in its entirety and then summarize by creating key takeaways of the overall content. The extractive Approach, however, deals with only word weights.

The abstractive Approach on the other hand uses NLP and DNN algorithms to build sequential and logical statements as humans would. NLP and DNNs offer better scope and quality results in comparison to the Extractive Approach.

Read - Whitepaper on how Deep Learning enabled Text Summarization enables audits

DNN sequencing in Text Summarization

DNNs use a sequence to sequence model while predicting a new sentence. One type of DNN is Long Short Term Memory (LTSM) that is used for Abstractive Text Summarization. LTSM is a recurring neural network. It uses LTSM cell blocks instead of neural network layers. It feeds the output of one LTSM block at time T as input to the same LTSM block at T+1. These neural networks are programmatically unraveled during the training of the algorithms. In this process, a new word or the output from an earlier cell is fed to the network at each time step. Thereby a new word is sequentially concatenated to the earlier output. This DNN framework predicts new words and sentences such that the LTSM progressively builds the Abstractive Text Summary. This framework uses a unique encode-decode model for building text summaries. This encode-decode model is trained in tandem to read the source and generate the summary.

Simply put

AI or more specifically NLP and DNN algorithms offer a competent model for creating summaries from vast arrays of unstructured documents. The model is adept at summarization of long documents and creating crisp summaries that can be attached as metadata and executive summaries to the digitized asset. In effect, the auto-summarization powered by AI makes auditing assignments quick and easy.