473 research outputs found
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
Generating a text abstract from a set of documents remains a challenging
task. The neural encoder-decoder framework has recently been exploited to
summarize single documents, but its success can in part be attributed to the
availability of large parallel data automatically acquired from the Web. In
contrast, parallel data for multi-document summarization are scarce and costly
to obtain. There is a pressing need to adapt an encoder-decoder model trained
on single-document summarization data to work with multiple-document input. In
this paper, we present an initial investigation into a novel adaptation method.
It exploits the maximal marginal relevance method to select representative
sentences from multi-document input, and leverages an abstractive
encoder-decoder model to fuse disparate sentences to an abstractive summary.
The adaptation method is robust and itself requires no training data. Our
system compares favorably to state-of-the-art extractive and abstractive
approaches judged by automatic metrics and human assessors.Comment: 11 page
Adapting End-to-End Speech Recognition for Readable Subtitles
Automatic speech recognition (ASR) systems are primarily evaluated on
transcription accuracy. However, in some use cases such as subtitling, verbatim
transcription would reduce output readability given limited screen size and
reading time. Therefore, this work focuses on ASR with output compression, a
task challenging for supervised approaches due to the scarcity of training
data. We first investigate a cascaded system, where an unsupervised compression
model is used to post-edit the transcribed speech. We then compare several
methods of end-to-end speech recognition under output length constraints. The
experiments show that with limited data far less than needed for training a
model from scratch, we can adapt a Transformer-based ASR model to incorporate
both transcription and compression capabilities. Furthermore, the best
performance in terms of WER and ROUGE scores is achieved by explicitly modeling
the length constraints within the end-to-end ASR system.Comment: IWSLT 202
A Novel ILP Framework for Summarizing Content with High Lexical Variety
Summarizing content contributed by individuals can be challenging, because
people make different lexical choices even when describing the same events.
However, there remains a significant need to summarize such content. Examples
include the student responses to post-class reflective questions, product
reviews, and news articles published by different news agencies related to the
same events. High lexical diversity of these documents hinders the system's
ability to effectively identify salient content and reduce summary redundancy.
In this paper, we overcome this issue by introducing an integer linear
programming-based summarization framework. It incorporates a low-rank
approximation to the sentence-word co-occurrence matrix to intrinsically group
semantically-similar lexical items. We conduct extensive experiments on
datasets of student responses, product reviews, and news documents. Our
approach compares favorably to a number of extractive baselines as well as a
neural abstractive summarization system. The paper finally sheds light on when
and why the proposed framework is effective at summarizing content with high
lexical variety.Comment: Accepted for publication in the journal of Natural Language
Engineering, 201
Diversity driven Attention Model for Query-based Abstractive Summarization
Abstractive summarization aims to generate a shorter version of the document
covering all the salient points in a compact and coherent fashion. On the other
hand, query-based summarization highlights those points that are relevant in
the context of a given query. The encode-attend-decode paradigm has achieved
notable success in machine translation, extractive summarization, dialog
systems, etc. But it suffers from the drawback of generation of repeated
phrases. In this work we propose a model for the query-based summarization task
based on the encode-attend-decode paradigm with two key additions (i) a query
attention model (in addition to document attention model) which learns to focus
on different portions of the query at different time steps (instead of using a
static representation for the query) and (ii) a new diversity based attention
model which aims to alleviate the problem of repeating phrases in the summary.
In order to enable the testing of this model we introduce a new query-based
summarization dataset building on debatepedia. Our experiments show that with
these two additions the proposed model clearly outperforms vanilla
encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.Comment: Accepted at ACL 201
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