  Multi-text applications, such as multi-document summarization, are typically
required to model redundancies across related texts. Current methods
confronting consolidation struggle to fuse overlapping information. In order to
explicitly represent content overlap, we propose to align predicate-argument
relations across texts, providing a potential scaffold for information
consolidation. We go beyond clustering coreferring mentions, and instead model
overlap with respect to redundancy at a propositional level, rather than merely
detecting shared referents. Our setting exploits QA-SRL, utilizing
question-answer pairs to capture predicate-argument relations, facilitating
laymen annotation of cross-text alignments. We employ crowd-workers for
constructing a dataset of QA-based alignments, and present a baseline QA
alignment model trained over our dataset. Analyses show that our new task is
semantically challenging, capturing content overlap beyond lexical similarity
and complements cross-document coreference with proposition-level links,
offering potential use for downstream tasks.

Computation and Language