Product Description and QA Assisted Self-Supervised Opinion Summarization (2024)

Abstract: In e-commerce, opinion summarization is the process of summarizing the consensus opinions found in product reviews. However, the potential of additional sources such as product description and question-answers (QA) has been considered less often. Moreover, the absence of any supervised training data makes this task challenging. To address this, we propose a novel synthetic dataset creation (SDC) strategy that leverages information from reviews as well as additional sources for selecting one of the reviews as a pseudo-summary to enable supervised training. Our Multi-Encoder Decoder framework for Opinion Summarization (MEDOS) employs a separate encoder for each source, enabling effective selection of information while generating the summary. For evaluation, due to the unavailability of test sets with additional sources, we extend the Amazon, Oposum+, and Flipkart test sets and leverage ChatGPT to annotate summaries. Experiments across nine test sets demonstrate that the combination of our SDC approach and MEDOS model achieves on average a 14.5% improvement in ROUGE-1 F1 over the SOTA. Moreover, comparative analysis underlines the significance of incorporating additional sources for generating more informative summaries. Human evaluations further indicate that MEDOS scores relatively higher in coherence and fluency with 0.41 and 0.5 (−1 to 1) respectively, compared to existing models. To the best of our knowledge, we are the first to generate opinion summaries leveraging additional sources in a self-supervised setting.

## introduction in the e-commerce domain, reviews play a vital role in making informed decisions. however, due to the recent proliferation of online reviews, going motivation in e-commerce, users' opinions are expressed through various sources such as product ratings, reviews, review upvotes and downvotes, and question-answers. additionally, for each product, description, product specification, product im-ages, price, etc. are present as well. considering such additional sources apart from reviews is vital in generating opinion summaries that are wellrounded and informative. specifically, descriptions offer nuanced details about various aspects, while question-answers provide additional perspectives on specific queries, both of which can be valuable. problem statement we propose a novel synthetic dataset creation approach that uses additional sources such as product description and questionanswers (qa) along with reviews for generating synthetic quadruplets of the form {input reviews, description, question-answers, pseudo-summary} to enable end-to-end supervised training. a multiencoder decoder model for opinion summarization (medos) to effectively select information from either product description or question-answers while summarizing reviews. for evaluation, due to the unavailability of test sets that have annotated summaries written considering such additional sources (except for flipkart @xcite )), we extend the available e-commerce test sets by including these additional sources and leveraging chat-gpt @xcite to annotate @xcite summaries. input: reviews, description, question-answers output: opinion summary our contributions are: ## problem formulation preliminaries. for a specific product or an entity, @xmath0, ..., r n } is the set of n reviews, d represents the product description, and @xmath1, ..., q m } represents a set of m question-answer pairs such that q i represents the i th concatenated question and its corresponding answer. ## synthetic dataset creation (sdc) before discussing the details of our framework, we formalize the synthetic dataset creation process used to train these models. in the absence of supervised datasets, most recent approaches @xcite resort to self-supervision wherein {input reviews, pseudo-summary} pairs are constructed. following @xcite , we can assume that a review @xmath2. this lets us create training points (t, r) i.e. {input reviews, pseudo-summary}, similar to what the model will experience during inference. t is fixed to size k, enabling comparison with existing works. however, in the presence of additional sources such as product description d and question-answer pairs q, we slightly modify this definition. instead of synthetic pairs, we construct synthetic quadruplets of the form: {input reviews, product description, question-answers, pseudo-summary}. algorithm 1 details the process of generating synthetic quadruplets. we generate multiple such quadruplets out of reviews r, product description d, and question-answer pairs q for a specific product. the overall idea for synthetic dataset creation is to choose relevant quadruplets for training. here we define relevance as the quadruplet that best aids our model in learning the task of opinion summarization using multiple sources. the intuition is to first select a pseudo-summary r that is the closest to both d and q. we measure closeness in terms of cosine similarity sim between their embeddings (sbert @xcite ). this selection ensures that the pseudo-summary r contains information relevant to both d and q so that the model learns to pick information from these two sources as well during training. next, using the pseudo-summary r selected, we look for its closest k set of reviews that can act as its input reviews set t , which ensures that the model learns the task of summarization. more formally, we first compute a matrix @xmath3, r b ) where r a , r @xmath4. we make all the diagonals of m as zero to remove self-comparisons using diag function. next, we compute @xmath5. we also compute @xmath6. finally, we compute @xmath7, λ 2 are parameters set to 0.5 for our experiments. we select r p ⊆ r reviews for forming p synthetic quadruplets by taking the top-p scores from ss. for each review @xmath8, we get the top-k reviews t from r -{r} using scores corresponding to the review r from m . this lets us form synthetic quadruplet on amazon test set and its extensions. r, d, q indicate the presence of reviews, description, and question-answers respectively in the input. abs? indicate abstractive systems. bold and underline indicate best and second-best scores using abstractive systems. * indicates @xmath9.05 on paired t-test against multimodalsum. overall our combination of sdc approach and medos outperforms baselines across all three test sets. instances such as {t, d, q, r} for model training. ## model framework (medos) figure 1 represents our multi-encoder framework, where each source passes through its separate encoder to generate separate attentions: the fused attention a f is then computed as: ## experiments 6.1 datasets we conducted experiments on: amazon @xcite , oposum+ (amplayo et al., 2021), and flipkart @xcite . statistics are in ## results and analysis automatic evaluation. we use the rouge-{1,2,l} f1 score @xcite ) (r1, r2 & rl) to assess the generated summary quality. tables 3 , 11 the vupoint film and slide scanner presents a mixed picture. it claims ease & efficiency for converting film to digital format, but user experiences differ. while some commend its user-friendliness and recommend it, others report frustrating issues like bleached images, compatibility problems, and subpar results. the need for xp compatibility limits its usefulness for modern systems. it offers potential for simple scanning but may require persistence to achieve desired outcomes. ## conclusion and future work we proposed a novel approach to create synthetic datasets by harnessing information from reviews and additional sources such as product description and question-answers. this method enables supervised training of models without the necessity of expensive annotated training datasets. our proposed framework medos uses separate encoders for selectively fusing information from these sources to generate an opinion summary. for evaluation, due to the absence of any test sets that contained such additional sources and annotated summaries, we extended the already available ecommerce test sets with additional sources and leveraged chatgpt to annotate summaries. this resulted in six additional test sets with 662 opinion summaries in total. results show that our synthetic dataset approach and medos framework outperforms the sota model on average by 14.5% and the simple input concatenation baseline by 6.5% across all nine test sets. through qualitative and comparative analysis we demonstrated that our model-generated summaries are more informative and emphasize the importance of including additional sources for comprehensive summaries. one future work is to expand these frameworks to encompass more reviews and all available sources, creating thorough product summaries.