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.
@cite_4: In this paper, we address the problem of generating a query-specific extractive summary in a an efficient manner for a given set of documents. In many of the current solutions, the entire collection of documents is modeled as a single graph which is used for summary generation. Unlike these approaches, in this paper, we model each individual document as a graph and generate a query-specific summary for it. These individual summaries are then intelligently combined to produce the final summary. This approach greatly reduces the computational complexity.
@cite_1: 
@cite_3: Web queries often give rise to a lot of documents and the user is overwhelmed by the information. Query-specific extractive summarization of a selected set of retrieved documents helps the user to get a gist of the information. The current extractive summary generation systems focus on extracting query-relevant sentences from the documents. However, the selected sentences are presented either in the order in which the documents were considered or in the order in which they were selected. This approach does not guarantee a coherent summary. In this paper, we propose incremental integrated graph to represent the sentences in a collection of documents. Sentences from the documents are merged into a master sequence to improve coherence and flow. The same ordering is used for sequencing the sentences in the extracted summary. User evaluations indicate that the proposed technique markedly improves the user satisfaction with regard to coherence in the summary.
@cite_0: Effective extraction of query relevant information present within documents on the web is a nontrivial task. In this paper we present our system called QueSTS, which does the above task by filtering and aggregating important query relevant sentences distributed across a set of documents. Our approach captures the contextual relationships among sentences of all input documents and represents them as an “integrated graph”. These relationships are exploited and several subgraphs of integrated graph which consist of sentences that are highly relevant to the query and that are highly related to each other are constructed. These subgraphs are ranked by our scoring model. The highest ranked subgraph which is rich in query relevant information and also has sentences that are highly coherent is returned as a query specific summary.
@cite_2: In this paper, we present a system called QueSTS, which generates a query specific extractive summary of a selected set of documents. We have proposed an integrated graph approach to represent the contextual relationships among sentences of all the input documents. These relationships are exploited and several sub-graphs of the integrated graph are constructed. These sub-graphs consist of sentences that are highly relevant to the query and that are highly related to each other. These sub-graphs are ranked by a scoring model. The highest ranked sub-graph which is rich in query relevant information is selected as a query specific summary. A sentence ordering strategy has also been proposed by us to improve the coherence of the summary. Sentences in the selected summary are sequenced as per the above strategy. Experimental results show that the summaries generated by the QueSTS system are significantly better than other systems in terms of user satisfaction.
Several models have been performed in the past to retrieve sentences of a document belonging to a particular topic @cite_1 . Given a topic, retrieving sentences that may belong to that topic should be considered as a different task than what we aim in this paper. A graph based approach for extracting information relevant to a query is presented in @cite_4 , where subgraphs are built using the relatedness of the sentences to the query. An incremental integrated graph to represent the sentences in a collection of documents is presented in @cite_0 @cite_2 . Sentences from the documents are merged into a master sequence to improve coherence and flow. The same ordering is used for sequencing the sentences in the extracted summary. Ordering of sentences in a document is discussed in @cite_3 . In this paper, we aim to generate the sections clusters from an unordered document. To the best of our knowledge, this is a first attempt to address this problem formally.