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Found 20 papers, 10 papers with code
Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization
In this article, we address this challenge by exploring a series of domain adaptation techniques.
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.
Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization
However, their application to chart-related tasks is not trivial as these tasks typically involve considering not only the underlying data but also the visual features in the chart image.
BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP
Large Language Models (LLMs) have emerged as one of the most important breakthroughs in NLP for their impressive skills in language generation and other language-specific tasks.
Abstractive Text Summarization
Natural Language Inference
+6
UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
Charts are very popular for analyzing data, visualizing key insights and answering complex reasoning questions about data.
Ranked #17 on
Chart Question Answering
on ChartQA
(using extra training data)
CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia
Debatepedia is a publicly available dataset consisting of arguments and counter-arguments on controversial topics that has been widely used for the single-document query-focused abstractive summarization task in recent years.
OpenCQA: Open-ended Question Answering with Charts
In the first setting, a chart and the accompanying article is provided as input to the model.
Chart Question Answering: State of the Art and Future Directions
Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights.
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions.
Ranked #1 on
Chart Question Answering
on RealCQA
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization
We also introduce a number of state-of-the-art neural models as baselines that utilize image captioning and data-to-text generation techniques to tackle two problem variations: one assumes the underlying data table of the chart is available while the other needs to extract data from chart images.
Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization
In this paper, we address this challenge by exploring a series of domain adaptation techniques.
ConVIScope: Visual Analytics for Exploring Patient Conversations
The proliferation of text messaging for mobile health is generating a large amount of patient-doctor conversations that can be extremely valuable to health care professionals.
Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection
We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13. 1% in the QA datasets and 18. 7% in the CQA datasets compared to the previous state-of-the-art.
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization
In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query.
Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
Information visualizations such as bar charts and line charts are very popular for exploring data and communicating insights.
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task
In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task.
Multimedia Summary Generation from Online Conversations: Current Approaches and Future Directions
With the proliferation of Web-based social media, asynchronous conversations have become very common for supporting online communication and collaboration.
An Interactive System for Exploring Community Question Answering Forums
We present an interactive system to provide effective and efficient search capabilities in Community Question Answering (cQA) forums.
