What is an unordered text document, do these arise in real-world corpora?
A unordered text document is one where sentences in the document are disordered or jumbled. It doesn't appear that unordered text documents appear in corpora, but rather are introduced as part of processing pipeline.
Segmenting an unordered text document into different sections is a very useful task in many text processing applications like multiple document summarization, question answering, etc. This paper proposes structuring of an unordered text document based on the keywords in the document. We test our approach on Wikipedia documents using both statistical and predictive methods such as the TextRank algorithm and Google's USE (Universal Sentence Encoder). From our experimental results, we show that the proposed model can effectively structure an unordered document into sections.
To structure an unordered document is an essential task in many applications. It is a post-requisite for applications like multiple document extractive text summarization where we have to present a summary of multiple documents. It is a prerequisite for applications like question answering from multiple documents where we have to present an answer by processing multiple documents. In this paper, we address the task of segmenting an unordered text document into different sections. The input document/summary that may have unordered sentences is processed so that it will have sentences clustered together. Clustering is based on the similarity with the respective keyword as well as with the sentences belonging to the same cluster. Keywords are identified and clusters are formed for each keyword. We use TextRank algorithm BIBREF0 to extract the keywords from a text document. TextRank is a graph-based ranking algorithm which decides the importance of a vertex within a graph by considering the global information recursively computed from the entire graph rather than focusing on local vertex specific information. The model uses knowledge acquired from the entire text to extract the keywords. A cluster is generated for every keyword. While generating clusters, the similarity between a sentence and a topic/keyword is calculated using the cosine similarity of embeddings generated using Google's USE (Universal Sentence Encoder) BIBREF1 . USE is claimed to have better performance of transfer learning when used with sentence-level embeddings as compared to word-level embeddings alone. This model is claimed to have better performance even if there is less task-specific training data available. We observed that the quality of clusters/section is better if the similarity of a sentence with the keyword is considered along with the similarity of the sentence with the sentences already available in the respective section. To test our approach, we jumble the ordering of sentences in a document, process the unordered document and compare the similarity of the output document with the original document.