Modelling Events through Memory-based, Open-IE Patterns for Abstractive Summarization
Abstract
Abstractive text summarization of news
requires a way of representing events, such
as a collection of pattern clusters in which
every cluster represents an event (e.g.,
marriage) and every pattern in the cluster
is a way of expressing the event (e.g.,
X married Y, X and Y tied the knot). We
compare three ways of extracting event
patterns: heuristics-based, compression-based
and memory-based. While the former
has been used previously in multi-document
abstraction, the latter two have
never been used for this task. Compared
with the first two techniques, the memory-based
method allows for generating significantly
more grammatical and informative
sentences, at the cost of searching a
vast space of hundreds of millions of parse
trees of known grammatical utterances. To
this end, we introduce a data structure and
a search method that make it possible to
efficiently extrapolate from every sentence
the parse sub-trees that match against any
of the stored utterances.
requires a way of representing events, such
as a collection of pattern clusters in which
every cluster represents an event (e.g.,
marriage) and every pattern in the cluster
is a way of expressing the event (e.g.,
X married Y, X and Y tied the knot). We
compare three ways of extracting event
patterns: heuristics-based, compression-based
and memory-based. While the former
has been used previously in multi-document
abstraction, the latter two have
never been used for this task. Compared
with the first two techniques, the memory-based
method allows for generating significantly
more grammatical and informative
sentences, at the cost of searching a
vast space of hundreds of millions of parse
trees of known grammatical utterances. To
this end, we introduce a data structure and
a search method that make it possible to
efficiently extrapolate from every sentence
the parse sub-trees that match against any
of the stored utterances.
Research Areas
