Query Focused Multi-Document Summarization with Distant Supervision

04/06/2020
by   Yumo Xu, et al.
0

We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS). Due to the lack of training data, existing work relies heavily on retrieval-style methods for estimating the relevance between queries and text segments. In this work, we leverage distant supervision from question answering where various resources are available to more explicitly capture the relationship between queries and documents. We propose a coarse-to-fine modeling framework which introduces separate modules for estimating whether segments are relevant to the query, likely to contain an answer, and central. Under this framework, a trained evidence estimator further discerns which retrieved segments might answer the query for final selection in the summary. We demonstrate that our framework outperforms strong comparison systems on standard QFS benchmarks.

READ FULL TEXT
12/14/2021

Scaling Up Query-Focused Summarization to Meet Open-Domain Question Answering

Query-focused summarization (QFS) requires generating a textual summary ...
11/03/2020

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 o...
12/29/2020

Abstractive Query Focused Summarization with Query-Free Resources

The availability of large-scale datasets has driven the development of n...
05/27/2021

Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance

Query focused summarization (QFS) models aim to generate summaries from ...
05/31/2021

Text Summarization with Latent Queries

The availability of large-scale datasets has driven the development of n...
11/09/2020

Distant Supervision for E-commerce Query Segmentation via Attention Network

The booming online e-commerce platforms demand highly accurate approache...
05/05/2020

Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering

We address the problem of extractive question answering using document-l...