Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTask Definition and Integration For Scientific-Document Writing Support
تعريف المهام وتكامل دعم كتابة المستندات العلمية
426
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
مع الزيادة في عدد الأوراق الأكاديمية المنشورة، تم وضع توقعات متزايدة على البحوث المتعلقة بدعم عملية الكتابة للأوراق العلمية. في الآونة الأخيرة، تم إجراء البحوث على مهام مختلفة مثل جدارة الاقتباس (الحكم على ما إذا كانت الجملة تتطلب الاقتباس) توصية الاقتباس وتوليد نص الاستشهاد. ومع ذلك، نظرا لأن كل مهمة تمت دراستها وتقييمها باستخدام البيانات التي تم تطويرها بشكل مستقل، فمن المستحيل حاليا التحقق مما إذا كانت هذه المهام يمكن أن تقطع أنابيبها بنجاح للاستخدام الفعال في كتابة المستندات العلمية. في هذه الورقة، نحدد أولا سلسلة من المهام المتعلقة بكتابة المستندات العلمية التي يمكن أن تكون خطاء. بعد ذلك، نقوم بإنشاء مجموعة بيانات من الأوراق الأكاديمية التي يمكن استخدامها لتقييم كل مهمة بالإضافة إلى سلسلة من هذه المهام. أخيرا، باستخدام DataSet، نقيم مهام جدارة الاقتباس وتوصية الاقتباس وكذلك كلتا المهام المتكاملة. توضح نتائج تقييماتنا أن النهج المقترح واعد.
With the increase in the number of published academic papers, growing expectations have been placed on research related to supporting the writing process of scientific papers. Recently, research has been conducted on various tasks such as citation worthiness (judging whether a sentence requires citation), citation recommendation, and citation-text generation. However, since each task has been studied and evaluated using data that has been independently developed, it is currently impossible to verify whether such tasks can be successfully pipelined to effective use in scientific-document writing. In this paper, we first define a series of tasks related to scientific-document writing that can be pipelined. Then, we create a dataset of academic papers that can be used for the evaluation of each task as well as a series of these tasks. Finally, using the dataset, we evaluate the tasks of citation worthiness and citation recommendation as well as both of these tasks integrated. The results of our evaluations show that the proposed approach is promising.
References used
https://aclanthology.org/
rate research
Read More
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle. Inspired by previous work which uses the Information Bottleneck principle for sentence compression, we exten
d it to document level summarization with two separate steps. In the first step, we use signal(s) as queries to retrieve the key content from the source document. Then, a pre-trained language model conducts further sentence search and edit to return the final extracted summaries. Importantly, our work can be flexibly extended to a multi-view framework by different signals. Automatic evaluation on three scientific document datasets verifies the effectiveness of the proposed framework. The further human evaluation suggests that the extracted summaries cover more content aspects than previous systems.
Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication. Most scholarly discourse involves interpreting experimental evidence and attempting to persuade other scien
tists to adopt the same conclusions. While various argument mining studies have addressed student essays and news articles, those that target scientific discourse are still scarce. This paper surveys existing work in argument mining of scholarly discourse, and provides an overview of current models, data, tasks, and applications. We identify a number of key challenges confronting argument mining in the scientific domain, and suggest some possible solutions and future directions.
Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks
that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.
Paraphrase generation is an important task in natural language processing. Previous works focus on sentence-level paraphrase generation, while ignoring document-level paraphrase generation, which is a more challenging and valuable task. In this paper
, we explore the task of document-level paraphrase generation for the first time and focus on the inter-sentence diversity by considering sentence rewriting and reordering. We propose CoRPG (Coherence Relationship guided Paraphrase Generation), which leverages graph GRU to encode the coherence relationship graph and get the coherence-aware representation for each sentence, which can be used for re-arranging the multiple (possibly modified) input sentences. We create a pseudo document-level paraphrase dataset for training CoRPG. Automatic evaluation results show CoRPG outperforms several strong baseline models on the BERTScore and diversity scores. Human evaluation also shows our model can generate document paraphrase with more diversity and semantic preservation.
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseu
do-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 2nd Workshop on Scholarly Document Processing (SDP) at NAACL 2021 as a virtual event (https://sdproc.org/2021/). The SDP workshop consisted of a research track, three invited talks, and three Shared Tasks (LongSumm 2021, SCIVER, and 3C). The program was geared towards the application of NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.
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
