Nonfactoid Question Answering as Query-Focused Summarization With Graph-Enhanced Multihop Inference.

Area of Science:

• Natural Language Processing (NLP)
• Artificial Intelligence (AI)

Background:

• Nonfactoid question answering (QA) presents significant challenges in NLP due to difficulties in capturing long-distance and complex semantic relations.
• Existing QA methods often struggle with intricate connections between questions and document content.

Purpose of the Study:

• To propose a novel query-focused summarization method, the graph-enhanced multihop query-focused summarizer (GMQS), specifically designed for nonfactoid QA.
• To enhance the handling of complex semantic relations and enable multihop reasoning in nonfactoid QA tasks.

Main Methods:

• Leveraging graph-enhanced reasoning techniques to elaborate the multihop inference process.
• Constructing three types of graphs (semantic relevance, topical coherence, coreference linking) to capture interrelationships between questions and documents.
• Developing a Relational Graph Attention Network (RGAT) for aggregating multirelational information.

Main Results:

• The GMQS method consistently outperforms existing extractive and abstractive methods on the WikiHow and PubMedQA datasets.
• Demonstrated superior performance in handling complex semantic relations and long-distance dependencies in nonfactoid QA.

Conclusions:

• The proposed GMQS method offers a significant advancement in nonfactoid question answering by effectively utilizing graph-enhanced reasoning.
• The method shows promise for explainable AI, enabling clear multihop reasoning pathways in QA systems.