Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWikiAsp: A Dataset for Multi-domain Aspect-based Summarization
WikiAiAIASP: مجموعة بيانات لتلخيص القائم على الجانب المجال
244
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
التلخصات القائمة على الجانب المجردة هي مهمة توليد ملخصات مركزة تستند إلى نقاط اهتمام محددة. هذه الملخصات تساعد تحليل فعال للنص، مثل فهم الاستعراضات أو الآراء بسرعة من زوايا مختلفة. ومع ذلك، نظرا للاختلافات الكبيرة في نوع الجوانب لمجالات مختلفة (مثل المشاعر، ميزات المنتج)، تميل تطوير النماذج السابقة إلى أن تكون خاصة بالمجال. في هذه الورقة، نقترح WikiAsp، 1 مجموعة بيانات واسعة النطاق لتلخيص القائم على الجانب متعدد المجالات التي تحاول تحفيز البحث في اتجاه التلخيص المستند إلى جانب النطاق. على وجه التحديد، نبني DataSet باستخدام مقالات Wikipedia من 20 مجالات مختلفة، باستخدام عناوين القسم وحدود كل مقال كوكيل للتعليق على الجانب. نقترح العديد من النماذج الأساسية المباشرة لهذه المهمة وإجراء تجارب على مجموعة البيانات. تسليط الضوء على النتائج التحديات الرئيسية التي تواجهها نماذج التلخيص الموجودة في هذا الإعداد، مثل التعامل مع الضمير المناسب للمصادر المعروضة والشرح المستمر للأحداث الحساسة للوقت.
Abstract Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text, such as quickly understanding reviews or opinions from different angles. However, due to large differences in the type of aspects for different domains (e.g., sentiment, product features), the development of previous models has tended to be domain-specific. In this paper, we propose WikiAsp,1 a large-scale dataset for multi-domain aspect- based summarization that attempts to spur research in the direction of open-domain aspect-based summarization. Specifically, we build the dataset using Wikipedia articles from 20 different domains, using the section titles and boundaries of each article as a proxy for aspect annotation. We propose several straightforward baseline models for this task and conduct experiments on the dataset. Results highlight key challenges that existing summarization models face in this setting, such as proper pronoun handling of quoted sources and consistent explanation of time-sensitive events.
References used
https://aclanthology.org/
rate research
Read More
Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks. However, its learning materials have been lacking a holistic organization by integrating resources from
various modalities, thereby lagging behind the research progress of this field. In this study, we release a full-scale multimodal dataset comprehensively gathering documents, summaries, images, captions, videos, audios, transcripts, and titles in English from CNN and Daily Mail. To our best knowledge, this is the first collection that spans all modalities and nearly comprises all types of materials available in this community. In addition, we devise a baseline model based on the novel dataset, which employs a newly proposed Jump-Attention mechanism based on transcripts. The experimental results validate the important assistance role of the external information for multimodal summarization.
This paper introduces MediaSum, a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. To create this dataset, we collect interview transcripts from NPR and CNN and employ the overview and topic description
s as summaries. Compared with existing public corpora for dialogue summarization, our dataset is an order of magnitude larger and contains complex multi-party conversations from multiple domains. We conduct statistical analysis to demonstrate the unique positional bias exhibited in the transcripts of televised and radioed interviews. We also show that MediaSum can be used in transfer learning to improve a model's performance on other dialogue summarization tasks.
Cross-lingual summarization is a challenging task for which there are no cross-lingual scientific resources currently available. To overcome the lack of a high-quality resource, we present a new dataset for monolingual and cross-lingual summarization
considering the English-German pair. We collect high-quality, real-world cross-lingual data from Spektrum der Wissenschaft, which publishes human-written German scientific summaries of English science articles on various subjects. The generated Spektrum dataset is small; therefore, we harvest a similar dataset from the Wikipedia Science Portal to complement it. The Wikipedia dataset consists of English and German articles, which can be used for monolingual and cross-lingual summarization. Furthermore, we present a quantitative analysis of the datasets and results of empirical experiments with several existing extractive and abstractive summarization models. The results suggest the viability and usefulness of the proposed dataset for monolingual and cross-lingual summarization.
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.
In this paper, we present a major update to the first Hungarian named entity dataset, the Szeged NER corpus. We used zero-shot cross-lingual transfer to initialize the enrichment of entity types annotated in the corpus using three neural NER models:
two of them based on the English OntoNotes corpus and one based on the Czech Named Entity Corpus finetuned from multilingual neural language models. The output of the models was automatically merged with the original NER annotation, and automatically and manually corrected and further enriched with additional annotation, like qualifiers for various entity types. We present the evaluation of the zero-shot performance of the two OntoNotes-based models and a transformer-based new NER model trained on the training part of the final corpus. We release the corpus and the trained model.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
