Multi-document Text Summarization using Semantic Word and Sentence Similarity: A Combined Approach (2021)

Abstract: The exponential growth in the number of text documents produced daily on the web poses several difficulties to people who are responsible for collecting, organizing, and searching different textual content related to a particular topic. Automatic Text Summarization works well in this direction, which can review many documents and pull out the relevant information. But the limitations associated with automatic text summarization need to be removed by finding efficient workarounds. Although current research works have focused on this direction for further improvements, they still face many challenges. This paper proposes a combined semantic-based word and sentence similarity approach to summarize a corpus of text documents. To arrange the sentences in the final summary, KL-divergence technique is used. The experimental work is conducted using DUC datasets, and the obtained results are promising.

## introduction with the widespread adaptation of technology, a large number of documents are getting digitized, resulting in a rapid influx of textual data. this data often contains crucial information; however, absorbing all this information can be difficult and time-consuming. automatic text summarization (ats) is the process of condensing data into useful and comprehensible information. by distilling out meaningful details, ats makes referring documents much more efficient. ats can be done in two ways: extractive and abstractive. extractive summarization selects sentences of importance directly from the source text, which can either be within a single document (called sin-gle document text summarization) or a group of documents (called multi-document text summarization) @xcite . on the other hand, abstractive text summarization is an understanding of the main concept of its expression in clear natural language. when abstraction is used for text summarization in deep learning issues, it can overcome the extractive method's grammatical inconsistencies. ## proposed approach assume a corpus p consists of d number of documents. at first, all d documents are merged into a single huge collection. all the sentences of this huge collection are extracted to form a set of sentences @xmath0, s 2 , s 3 , • • • , s n }. below steps discuss how the coherent summary is generated from the corpus p . the depth of the subsumer is determined by counting the levels from the subsumer to the hierarchical net's top. words in the top layers of the hierarchy have a broader meaning and fewer semantic concepts than words in the lower layers. when computing the similarity, this behavior must be taken into account. thus, it is necessary to scale up the sim(a, b) for subsuming words at bottom layers, and for subsuming words at higher layers, one needs to scale down the sim(a, b). this shows f 2 (depth-sub) of equation 1 should be monotonically increasing function as shown illustrated in equation 3. ## analysis of experimental results the description of duc datasets foot_6 used for experimental purposes are shown in ## conclusion by combining semantic-based word and sentence similarity, the proposed method suggested a novel figure 7: readbility of summary (duc-2002) figure 8: readbility of summary (duc-2006) extractive text summarisation technique. modified harmonic mean is used to select the important words of each sentence, and then the sentence similarity is computed. based on the cohesion score, top sentences are selected that constitute the final summary. the sentences in the final summary are organized using kl-divergence approach. the proposed method's experimental work is carried out on two duc datasets. the proposed approach outperforms the standard approaches on duc-2006 and duc-2002 datasets, the proposed model can use the medical data as the input documents to generate a useful summary that can help the medical system.