@inproceedings{korkankar-etal-2024-aspect,
title = "Aspect-based Summaries from Online Product Reviews: A Comparative Study using various {LLM}s",
author = "Korkankar, Pratik Deelip and
Abranches, Alvyn and
Bhagat, Pradnya and
Pawar, Jyoti",
editor = "Lalitha Devi, Sobha and
Arora, Karunesh",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-1.65/",
pages = "562--568",
abstract = "In the era of online shopping, the volume of product reviews for user products on e-commerce platforms is massively increasing on a daily basis. For any given user product, it consists of a flood of reviews and manually analysing each of these reviews to understand the important aspects or opinions associated with the products is difficult and time-consuming task. Furthermore, it becomes nearly impossible for the customer to make decision of buying the product or not. Thus, it becomes necessary to have an aspect-based summary generated from these user reviews, which can act as a guide for the interested buyer in decision-making. Recently, the use of Large Language Models (LLMs) has shown great potential for solving diverse Natural Language Processing (NLP) tasks, including the task of summarization. Our paper explores the use of various LLMs such as Llama3, GPT-4o, Gemma2, Mistral, Mixtral and Qwen2 on the publicly available domain-specific Amazon reviews dataset as a part of our experimentation work. Our study postulates an algorithm to accurately identify product aspects and the model`s ability to extract relevant information and generate concise summaries. Further, we analyzed the experimental results of each of these LLMs with summary evaluation metrics such as Rouge, Meteor, BERTScore F1 and GPT-4o to evaluate the quality of the generated aspect-based summary. Our study highlights the strengths and limitations of each of these LLMs, thereby giving valuable insights for guiding researchers in harnessing LLMs for generating aspect-based summaries of user products present on these online shopping platforms."
}
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<abstract>In the era of online shopping, the volume of product reviews for user products on e-commerce platforms is massively increasing on a daily basis. For any given user product, it consists of a flood of reviews and manually analysing each of these reviews to understand the important aspects or opinions associated with the products is difficult and time-consuming task. Furthermore, it becomes nearly impossible for the customer to make decision of buying the product or not. Thus, it becomes necessary to have an aspect-based summary generated from these user reviews, which can act as a guide for the interested buyer in decision-making. Recently, the use of Large Language Models (LLMs) has shown great potential for solving diverse Natural Language Processing (NLP) tasks, including the task of summarization. Our paper explores the use of various LLMs such as Llama3, GPT-4o, Gemma2, Mistral, Mixtral and Qwen2 on the publicly available domain-specific Amazon reviews dataset as a part of our experimentation work. Our study postulates an algorithm to accurately identify product aspects and the model‘s ability to extract relevant information and generate concise summaries. Further, we analyzed the experimental results of each of these LLMs with summary evaluation metrics such as Rouge, Meteor, BERTScore F1 and GPT-4o to evaluate the quality of the generated aspect-based summary. Our study highlights the strengths and limitations of each of these LLMs, thereby giving valuable insights for guiding researchers in harnessing LLMs for generating aspect-based summaries of user products present on these online shopping platforms.</abstract>
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%0 Conference Proceedings
%T Aspect-based Summaries from Online Product Reviews: A Comparative Study using various LLMs
%A Korkankar, Pratik Deelip
%A Abranches, Alvyn
%A Bhagat, Pradnya
%A Pawar, Jyoti
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F korkankar-etal-2024-aspect
%X In the era of online shopping, the volume of product reviews for user products on e-commerce platforms is massively increasing on a daily basis. For any given user product, it consists of a flood of reviews and manually analysing each of these reviews to understand the important aspects or opinions associated with the products is difficult and time-consuming task. Furthermore, it becomes nearly impossible for the customer to make decision of buying the product or not. Thus, it becomes necessary to have an aspect-based summary generated from these user reviews, which can act as a guide for the interested buyer in decision-making. Recently, the use of Large Language Models (LLMs) has shown great potential for solving diverse Natural Language Processing (NLP) tasks, including the task of summarization. Our paper explores the use of various LLMs such as Llama3, GPT-4o, Gemma2, Mistral, Mixtral and Qwen2 on the publicly available domain-specific Amazon reviews dataset as a part of our experimentation work. Our study postulates an algorithm to accurately identify product aspects and the model‘s ability to extract relevant information and generate concise summaries. Further, we analyzed the experimental results of each of these LLMs with summary evaluation metrics such as Rouge, Meteor, BERTScore F1 and GPT-4o to evaluate the quality of the generated aspect-based summary. Our study highlights the strengths and limitations of each of these LLMs, thereby giving valuable insights for guiding researchers in harnessing LLMs for generating aspect-based summaries of user products present on these online shopping platforms.
%U https://aclanthology.org/2024.icon-1.65/
%P 562-568
Markdown (Informal)
[Aspect-based Summaries from Online Product Reviews: A Comparative Study using various LLMs](https://aclanthology.org/2024.icon-1.65/) (Korkankar et al., ICON 2024)
ACL