A nifty review to text summarization-based recommendation system for electronic products

Roul, Rajendra Kumar; Arora, Kushagr

doi:10.1007/s00500-019-03861-3

Cited by 10 publications

(2 citation statements)

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“…The 2010s witnessed the emergence of graph-based algorithms, which represented textual elements and their relationships as graph structures. Roul and Arora (Roul and Arora, 2019) employed fuzzy c-means clustering and a graph representation of user reviews to generate concise summaries for product recommendations. Alguliyev et al (Alguliyev et al, 2019) introduced a two-stage approach, combining clustering and optimization -initially grouping related sentences using k-means clustering, followed by an optimization model to extract the most salient sentence from each cluster, ensuring comprehensive yet concise summaries.…”

Section: Graph-based Algorithmsmentioning

confidence: 99%

“…Sankarasubramaniam et al (Sankarasubramaniam et al, 2014) generated query-specific concept graphs using Wikipedia to rank sentences, allowing the summaries to be focused on user queries. Roul and Arora (Roul and Arora, 2019) clustered reviews into aspects, enabling summarization of desired product features based on user queries. Querybased summarization enables summarization systems to provide targeted, user-centric summaries on-demand, aligning with human capabilities.…”

Section: Multilingual and Query-based Summarizationmentioning

confidence: 99%

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Automatic Text Summarization Using Deep Learning Methods

Shopnil,

Das,

Saha

2024

Preprint

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Text document summarization is critical for managing today’s vast textual data. This paper presents an approach to text document summarization that does not rely on word embedding techniques. Instead, our method follows a step-by-step process, including sentence segmentation, sentence embedding, K-means clustering, and summary generation. The input text is segmented into individual sentences using an NLP tool such as NLTK’s sentence tokenizer. Next, we extract contextual embeddings for each sentence using the Sentence Transformer method. These embeddings capture the meaning of each sentence within the context of the surrounding text. The sentence embeddings are then subjected to K-means clustering. This step enables the creation of clusters that represent semantically related sentences. To generate the summary, depending on how far each sentence is from the cluster centroid, we choose one sentence from each cluster. The sentence with the lowest distance from the centroid is chosen, and the selected sentences are ordered as they appeared in the original text. We implemented the summarizer and evaluated its performance on the DUC 2007 dataset, a collection of news articles with manually crafted summaries by human experts. The results demonstrate that our summarizer produces informative and concise summaries, surpassing a baseline approach that solely extracts top-ranked sentences from the input text. Our work contributes to text document summarization by presenting an alternative approach that does not rely on word embedding techniques. By leveraging sentence segmentation, contextual embeddings, K-means clustering, and centroid-based selection, our method offers a viable solution for generating high-quality summaries. Further research can explore enhancements to our approach and its application in various domains where text summarization is essential.

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Section: Graph-based Algorithmsmentioning

confidence: 99%

Section: Multilingual and Query-based Summarizationmentioning

confidence: 99%