NewsQs: Multi-Source Question Generation for News Summarization

Core Concepts

The author presents NewsQs, a dataset of question-answer pairs for news documents, generated using a T5-Large model fine-tuned on FAQ-style news articles. The approach focuses on creating high-quality questions to aid query-based multi-document summarization.

Abstract

NewsQs is a dataset created by augmenting the Multi-News dataset with automatically generated questions using a T5-Large model. The goal is to provide resources for query-based multi-document summarization by generating relevant questions from news articles. The study shows that fine-tuning the model with control codes improves question quality and correlation with human annotations.
Key points include the challenges in curating datasets for answering questions about news events, the process of generating questions from existing datasets, and the importance of high-quality questions in multi-document summarization tasks. The research highlights the significance of control codes in improving question generation quality and filtering low-quality examples using a QNLI model.
The study also discusses related work in query-based multi-document summarization datasets, limitations inherited from existing datasets, ethical considerations in dataset creation, and future implications for NLP technology advancement.

Stats

A dataset of 21,000 high-quality question-answer pairs for multiple documents is released.
Average QNLI score improved from 0.387 to 0.396 after filtering low-quality examples.
Average length of answers in the NewsQs dataset is 287 words.
Percentage overlap of entities between questions and answers is around 44.1%.

Quotes

Key Insights Distilled From

NewsQs

by Alyssa Hwang... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18479.pdf

Deeper Inquiries

How can the use of control codes impact question generation beyond this specific study?

Control codes play a crucial role in guiding language models during fine-tuning to generate more relevant and contextually appropriate questions. Beyond this study, the use of control codes can significantly enhance question generation in various ways:

Improved Relevance: Control codes help direct the model's attention towards specific aspects or entities within the input text, leading to questions that are more closely related to the content. This ensures that generated questions are focused and pertinent.

Enhanced Diversity: By incorporating different control codes representing distinct topics or entities, models can produce a diverse set of questions covering various aspects of the input data. This diversity is essential for generating comprehensive sets of questions across different contexts.

Fine-Tuned Specificity: Control codes allow for fine-tuning models on particular domains or types of content, enabling them to generate specialized questions tailored to specific datasets or tasks. This specificity enhances the quality and relevance of generated questions.

Consistent Question Style: Using control codes helps maintain consistency in question style and structure across different instances, ensuring coherence in question generation outputs.

Scalability and Adaptability: Control codes provide a scalable approach to adapt existing pre-trained models for new tasks or datasets by simply adjusting the control signals used during fine-tuning. This flexibility makes it easier to apply similar techniques in diverse NLP applications.

What are potential drawbacks or biases introduced by relying on existing datasets like Multi-News?

While leveraging existing datasets like Multi-News offers numerous advantages for research purposes, there are several potential drawbacks and biases associated with relying solely on such resources:

Quality Limitations: Existing datasets may contain errors, inconsistencies, or biases introduced during data collection or annotation processes, impacting the overall quality and reliability of results derived from these datasets.

Domain Specificity: Datasets like Multi-News may be domain-specific, limiting their generalizability across different domains or real-world applications where varied data sources are involved.

Data Skewness: The composition of existing datasets may exhibit skewness towards certain topics, perspectives, or sources due to how they were curated initially. This skewness can lead to biased model training and evaluation outcomes.

4Limited Coverage: Existing datasets might not encompass all possible scenarios or variations present in real-world data settings; thus, models trained solely on such limited data may struggle when faced with novel situations outside dataset boundaries
5Ethical Concerns: Biases inherent in existing datasets could perpetuate stereotypes,
discrimination
or misinformation if not carefully addressed during model development

How can human-centered annotation tasks be further improved

to enhance evaluation processes in NLP research?
Human-centered annotation tasks play a vital role in evaluating NLP systems' performance accurately.
Here are some strategies that could further improve human-centered annotation tasks:
1Clear Guidelines: Provide annotators with detailed guidelines outlining task objectives,
expected outcomes
and criteria for judgment.This clarity ensures consistency among annotators
and reduces ambiguity.
2Training: Offer comprehensive training sessions before starting annotations,
focusing on task understanding,
labeling conventions,and examples.This equips annotators with necessary skills
and knowledge neededfor accurate evaluations.
3Iterative Feedback Loop: Establish an iterative feedback loop where annotators receive regular feedbackon their annotations.This process helps address any discrepancies early-on,reinforces correct practices,and improves overall quality over time.
4Diverse Annotator Pool: Ensure diversity among annotators regarding demographics,cultural backgrounds,and expertise.Different perspectivescan enrich evaluations,detect biases,and offer insights from varying viewpointsenhancing evaluation robustness.
5*Adaptive Annotation Tools: Implement adaptive tools that assistannotatorsduring complexevaluationtasks,such as highlighting key information,suggesting labels basedon context,and providing explanations.These tools streamlineannotationprocessesandimprove efficiencywhile maintaining accuracy.
These enhancements will contribute towards more reliable,human-centric evaluationsinNLPresearch,resultingin higher-qualitydatasetsandalgorithmswith broaderapplicabilityandreliability