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Register a new userSemantic-Based Opinion Summarization
تلخيص الرأي الدلالي
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يمكن أن تكون كمية المعلومات المتاحة عبر الإنترنت ساحقة للمستخدمين من هضمها، خاصة عند التعامل مع تعليقات المستخدمين الآخرين عند اتخاذ قرار بشأن شراء منتج أو خدمة. في هذا السياق، تكون أنظمة تلخيص الرأي ذات قيمة كبيرة، واستخراج معلومات مهمة من النصوص وتقديمها للمستخدم بطريقة أكثر فهمة. من المعروف أيضا أن استخدام التمثيلات الدلالية يمكن أن يفيدن جودة الملخصات التي تم إنشاؤها. تهدف هذه الورقة إلى تطوير أساليب تلخيص الرأي بناء على مجردة معنى تمثيل النصوص في اللغة البرتغالية البرازيلية. تم التحقيق في أربع طرق مختلفة، إلى جانب بعض مناهج الأدب. تظهر النتائج أن الأسلوب المستند إلى جهاز التعلم الآلي أنتج ملخصات ذات جودة أعلى، مما يتفوق على تقنيات الأدب الأخرى على الرسوم البيانية الدلالية المصنوعة يدويا. نعرض أيضا أن استخدام الرسوم البيانية المحيطة بها أكثر من تلك المشروح يدويا ضرر بالإخراج. أخيرا، يشير تحليل مدى أهمية أنواع المعلومات المختلفة لعملية التلخيص إلى أن استخدام ميزات تحليل المعرفات لم يحسن جودة ملخص.
The amount of information available online can be overwhelming for users to digest, specially when dealing with other users' comments when making a decision about buying a product or service. In this context, opinion summarization systems are of great value, extracting important information from the texts and presenting them to the user in a more understandable manner. It is also known that the usage of semantic representations can benefit the quality of the generated summaries. This paper aims at developing opinion summarization methods based on Abstract Meaning Representation of texts in the Brazilian Portuguese language. Four different methods have been investigated, alongside some literature approaches. The results show that a Machine Learning-based method produced summaries of higher quality, outperforming other literature techniques on manually constructed semantic graphs. We also show that using parsed graphs over manually annotated ones harmed the output. Finally, an analysis of how important different types of information are for the summarization process suggests that using Sentiment Analysis features did not improve summary quality.
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Abstract We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization. QT is inspired by Vector- Quantized Variational Autoencoders, which we repurpose for popularity-driven summarization. It uses a clusterin
g interpretation of the quantized space and a novel extraction algorithm to discover popular opinions among hundreds of reviews, a significant step towards opinion summarization of practical scope. In addition, QT enables controllable summarization without further training, by utilizing properties of the quantized space to extract aspect-specific summaries. We also make publicly available Space, a large-scale evaluation benchmark for opinion summarizers, comprising general and aspect-specific summaries for 50 hotels. Experiments demonstrate the promise of our approach, which is validated by human studies where judges showed clear preference for our method over competitive baselines.
Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them. In this paper, we propose an approach that allows the generation of customized summaries based on aspect
queries (e.g., describing the location and room of a hotel). Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers which are induced by a multi-instance learning model that predicts the aspects of a document at different levels of granularity. We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers. Experiments on two benchmarks show that our model outperforms the previous state of the art and generates personalized summaries by controlling the number of aspects discussed in them.
ROUGE is a widely used evaluation metric in text summarization. However, it is not suitable for the evaluation of abstractive summarization systems as it relies on lexical overlap between the gold standard and the generated summaries. This limitation
becomes more apparent for agglutinative languages with very large vocabularies and high type/token ratios. In this paper, we present semantic similarity models for Turkish and apply them as evaluation metrics for an abstractive summarization task. To achieve this, we translated the English STSb dataset into Turkish and presented the first semantic textual similarity dataset for Turkish as well. We showed that our best similarity models have better alignment with average human judgments compared to ROUGE in both Pearson and Spearman correlations.
Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding. To address this
question, we propose a novel end-to-end Transformer-based model FinDS for abstractive dialogue summarization that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generates better summaries. Experiments on the SAMsum dataset show that FinDS outperforms various dialogue summarization approaches and achieves new state-of-the-art (SOTA) ROUGE results. Finally, we apply FinDS to a more complex scenario, showing the robustness of our model. We also release our source code.
In this tutorial, we will show where we are and where we will be to those researchers interested in this topic. We divide this tutorial into three parts, including coarse-grained financial opinion mining, fine-grained financial opinion mining, and po
ssible research directions. This tutorial starts by introducing the components in a financial opinion proposed in our research agenda and summarizes their related studies. We also highlight the task of mining customers' opinions toward financial services in the FinTech industry, and compare them with usual opinions. Several potential research questions will be addressed. We hope the audiences of this tutorial will gain an overview of financial opinion mining and figure out their research directions.
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