HEADS: Headline Generation as Sequence Prediction Using an Abstract Feature-Rich Space

By Carlos A. Colmenares, Marina Litvak, Amin Mantrach, Fabrizio Silvestri. 2015

53 citations recorded by the source corpus.

Automatic headline generation is a sub-task of document summarization with many reported applications. In this study we present a sequence-prediction technique for learning how editors title their news stories. The introduced technique models the problem as a discrete optimization task in a feature-rich space. In this space the global optimum can be found in polynomial time by means of dynamic programming. We train and test our model on an extensive corpus of financial news, and compare it against a number of baselines by using standard metrics from the document summarization domain, as well as some new ones proposed in this work. We also assess the readability and informativeness of the generated titles through human evaluation. The obtained results are very appealing and substantiate the soundness of the approach.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

A Deep Reinforced Model for Abstractive Summarization (2017, background): “Most summarization models studied in the past are extractive in nature (Neto et al., 2002; Dorr et al., 2003; Filippova and Altun, 2013; Colmenares et al., 2015; Nallapati et al., 2017), which usually work by identifying the most important phrases of an input document and re-arranging them into a ne”
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond (2016, background): “A vast majority of the past work in summarization has been extractive, which consists of identifying key sentences or passages in the source document and reproducing them as summary (Neto et al., 2002; Erkan and Radev, 2004; Wong et al., 2008; Filippova and Altun, 2013; Colmenares et al., 2015).”
Neural Text Summarization: A Critical Evaluation (2019, background): “Non-neural approaches (Neto et al., 2002; Dorr et al., 2003; Filippova and Altun, 2013; Colmenares et al., 2015) utilized domain expertise to develop heuristics for summary content selection, whereas more recent, neural techniques allow for end-to-end training.”
LCSTS: A Large Scale Chinese Short Text Summarization Dataset (2015, background): “3 million financial news headline dataset written in English for headline generation (Colmenares et al., 2015).”
Improving Abstraction in Text Summarization (2018, methodology): “In extractive summarization, the model selects passages from the input document and combines them to form a shorter summary, sometimes with a post-processing step to ensure final coherence of the output (Neto et al., 2002; Dorr et al., 2003; Filippova and Altun, 2013; Colmenares et al., 2015; Nallap”
Abstractive text summarization using LSTM-CNN based deep learning (2018, background): “Sumit Chopra [7] introduces a conditional recurrent neural network (RNN) which generates a summary of an input sentence.”