Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userIceSum: An Icelandic Text Summarization Corpus
acesum: تخصيص نص أيسلندي كوربوس
118
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تلخيص النص التلقائي (ATS) هو مهمة توليد ملخصات موجزة وطلاقة من مستند واحد أو أكثر.في هذه الورقة، نقدم ICESUM، أول كوربوس الأيسلندية المشروح مع ملخصات تولدها الإنسان.يتكون ICESUM من 1000 مقالة إخبارية عبر الإنترنت ملخصاتها الاستخراجية.نحن ندرب وتقييم العديد من النماذج القائمة على الشبكة العصبية في هذه البيانات، ومقارنتها ضد مجموعة مختارة من الأساليب الأساسية.نجد أن نموذج فك ترميز التشفير مع النازع المستند إلى التسلسل يحصل على أفضل النتائج، مما يتفوق على جميع أساليب خط الأساس.علاوة على ذلك، نقيم كيف يؤثر حجم كوربوس التدريب على جودة الملخصات التي تم إنشاؤها.نفرج عن Corpus والنماذج مع ترخيص مفتوح.
Automatic Text Summarization (ATS) is the task of generating concise and fluent summaries from one or more documents. In this paper, we present IceSum, the first Icelandic corpus annotated with human-generated summaries. IceSum consists of 1,000 online news articles and their extractive summaries. We train and evaluate several neural network-based models on this dataset, comparing them against a selection of baseline methods. We find that an encoder-decoder model with a sequence-to-sequence based extractor obtains the best results, outperforming all baseline methods. Furthermore, we evaluate how the size of the training corpus affects the quality of the generated summaries. We release the corpus and the models with an open license.
References used
https://aclanthology.org/
rate research
Read More
In machine translation, corpus preparation is one of the crucial tasks, particularly for lowresource pairs. In multilingual countries like India, machine translation plays a vital role in communication among people with various linguistic backgrounds
. There are available online automatic translation systems by Google and Microsoft which include various languages which lack support for the Khasi language, which can hence be considered lowresource. This paper overviews the development of EnKhCorp1.0, a corpus for English--Khasi pair, and implemented baseline systems for EnglishtoKhasi and KhasitoEnglish translation based on the neural machine translation approach.
Social media (SM) platforms such as Twitter provide large quantities of real-time data that can be leveraged during mass emergencies. Developing tools to support crisis-affected communities requires available datasets, which often do not exist for lo
w resource languages. This paper introduces Kawarith a multi-dialect Arabic Twitter corpus for crisis events, comprising more than a million Arabic tweets collected during 22 crises that occurred between 2018 and 2020 and involved several types of hazard. Exploration of this content revealed the most discussed topics and information types, and the paper presents a labelled dataset from seven emergency events that serves as a gold standard for several tasks in crisis informatics research. Using annotated data from the same event, a BERT model is fine-tuned to classify tweets into different categories in the multi- label setting. Results show that BERT-based models yield good performance on this task even with small amounts of task-specific training data.
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Recent studies have leveraged gr
aph neural networks to capture the inter-sentential relationship (e.g., the discourse graph) within the documents to learn contextual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity and natural connection relationships), nor model intra-sentential relationships (e.g, semantic similarity and syntactic relationship among words). To address these problems, we propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive text summarization. Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate effectiveness of our method.
We create a large-scale dialogue corpus that provides pragmatic paraphrases to advance technology for understanding the underlying intentions of users. While neural conversation models acquire the ability to generate fluent responses through training
on a dialogue corpus, previous corpora have mainly focused on the literal meanings of utterances. However, in reality, people do not always present their intentions directly. For example, if a person said to the operator of a reservation service I don't have enough budget.'', they, in fact, mean please find a cheaper option for me.'' Our corpus provides a total of 71,498 indirect--direct utterance pairs accompanied by a multi-turn dialogue history extracted from the MultiWoZ dataset. In addition, we propose three tasks to benchmark the ability of models to recognize and generate indirect and direct utterances. We also investigated the performance of state-of-the-art pre-trained models as baselines.
This is a research proposal for doctoral research into sarcasm detection, and the real-time compilation of an English language corpus of sarcastic utterances. It details the previous research into similar topics, the potential research directions and the research aims.
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
