81,863 research outputs found
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text
Automatic question generation (QG) is a useful yet challenging task in NLP.
Recent neural network-based approaches represent the state-of-the-art in this
task. In this work, we attempt to strengthen them significantly by adopting a
holistic and novel generator-evaluator framework that directly optimizes
objectives that reward semantics and structure. The {\it generator} is a
sequence-to-sequence model that incorporates the {\it structure} and {\it
semantics} of the question being generated. The generator predicts an answer in
the passage that the question can pivot on. Employing the copy and coverage
mechanisms, it also acknowledges other contextually important (and possibly
rare) keywords in the passage that the question needs to conform to, while not
redundantly repeating words. The {\it evaluator} model evaluates and assigns a
reward to each predicted question based on its conformity to the {\it
structure} of ground-truth questions. We propose two novel QG-specific reward
functions for text conformity and answer conformity of the generated question.
The evaluator also employs structure-sensitive rewards based on evaluation
measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG. In
contrast, most of the previous works only optimize the cross-entropy loss,
which can induce inconsistencies between training (objective) and testing
(evaluation) measures. Our evaluation shows that our approach significantly
outperforms state-of-the-art systems on the widely-used SQuAD benchmark as per
both automatic and human evaluation.Comment: 10 pages, The SIGNLL Conference on Computational Natural Language
Learning (CoNLL 2019
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Generating Distractors for Reading Comprehension Questions from Real Examinations
We investigate the task of distractor generation for multiple choice reading
comprehension questions from examinations. In contrast to all previous works,
we do not aim at preparing words or short phrases distractors, instead, we
endeavor to generate longer and semantic-rich distractors which are closer to
distractors in real reading comprehension from examinations. Taking a reading
comprehension article, a pair of question and its correct option as input, our
goal is to generate several distractors which are somehow related to the
answer, consistent with the semantic context of the question and have some
trace in the article. We propose a hierarchical encoder-decoder framework with
static and dynamic attention mechanisms to tackle this task. Specifically, the
dynamic attention can combine sentence-level and word-level attention varying
at each recurrent time step to generate a more readable sequence. The static
attention is to modulate the dynamic attention not to focus on question
irrelevant sentences or sentences which contribute to the correct option. Our
proposed framework outperforms several strong baselines on the first prepared
distractor generation dataset of real reading comprehension questions. For
human evaluation, compared with those distractors generated by baselines, our
generated distractors are more functional to confuse the annotators.Comment: AAAI201
A Deep Generative Framework for Paraphrase Generation
Paraphrase generation is an important problem in NLP, especially in question
answering, information retrieval, information extraction, conversation systems,
to name a few. In this paper, we address the problem of generating paraphrases
automatically. Our proposed method is based on a combination of deep generative
models (VAE) with sequence-to-sequence models (LSTM) to generate paraphrases,
given an input sentence. Traditional VAEs when combined with recurrent neural
networks can generate free text but they are not suitable for paraphrase
generation for a given sentence. We address this problem by conditioning the
both, encoder and decoder sides of VAE, on the original sentence, so that it
can generate the given sentence's paraphrases. Unlike most existing models, our
model is simple, modular and can generate multiple paraphrases, for a given
sentence. Quantitative evaluation of the proposed method on a benchmark
paraphrase dataset demonstrates its efficacy, and its performance improvement
over the state-of-the-art methods by a significant margin, whereas qualitative
human evaluation indicate that the generated paraphrases are well-formed,
grammatically correct, and are relevant to the input sentence. Furthermore, we
evaluate our method on a newly released question paraphrase dataset, and
establish a new baseline for future research
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