• summarisation
  • Distill the most important information from a text to produce shortened or abridged version
  • Examples
    • outlines of a document
    • abstracts of a scientific article
    • headlines of a news article
    • snippets of search result
  • what to summarise
    • Single-document summarisation
      • Input: a single document
      • Output: summary that characterise the content
    • Multi-document summarisation（find overlapping information）
      • Input: multiple documents
      • Output: summary that captures the gist(要点，主旨) of all documents
      • E.g. summarise a news event from multiple sources or perspectives
  • how to summarise
    • Extractive summarisation
      • Summarise by selecting representative sentences from documents
    • Abstractive summarisation
      • Summarise the content in your own words
      • Summaries will often be paraphrases of the original content
  • goal of summarisation
    • Generic summarisation
      • Summary gives important information in the document(s)
    • Query-focused summarisation
      • Summary responds to a user query
      • “Non-factoid” QA
      • Answer is much longer than factoid QA

Extractive: Single-Doc

• summarisation system

  • Content selection: select what sentences to extract from the document

  • Information ordering: decide how to order extracted sentences

  • Sentence realisation: cleanup to make sure combined sentences are fluent

  • We will focus on content selection

  • For single-document summarisation, information ordering not necessary

    • present extracted sentences in original order

  • Sentence realisation also not necessary if they are presented in dot points

• content selection

  • Not much data with ground truth extractive sentences

  • Mostly unsupervised methods

  • Goal: Find sentences that are important or salient(显著的，突出的)

  • method1: TF-IDF

    • Frequent words in a doc $\to$ salient
    • But some generic words are very frequent but uninformative
      • function words
      • stop words
    • Weigh each word $w$ in document $d$ by its inverse document frequency:
      • $weight(w)=t{f}_{d,w}\times id{f}_w$

  • method 2: Log Likelihood Ratio

    • Intuition: a word is salient if its probability in the input corpus is very different to a background corpus(e.g. Wikipedia)
    • $f(n)=\{\begin{array}{ll}1,& if-2log\lambda (w)>10\\ 0,& otherwise\end{array}$
    • $\lambda (w)$ is the ratio between:
      • numerator: P(observing $w$ in $I$) $\to \left(\begin{array}{c}{N}_I\\ x\end{array}\right)p^x(1-p{)}^{N_I-x}$ and P(observing $w$ in $B$) $\to \left(\begin{array}{c}{N}_B\\ y\end{array}\right)p^y(1-p{)}^{N_B-y}$, assuming $P(w|I)=P(w|B)=p\to \frac{x+y}{N_I+N_B}$
      • denominato: P(observing $w$ in $I$) $\to \left(\begin{array}{c}{N}_I\\ x\end{array}\right)p_I^{x_I}(1-p_I{)}^{N_I-x}$ and P(observing $w$ in $B$) $\to \left(\begin{array}{c}{N}_B\\ y\end{array}\right)p_B^y(1-p_B{)}^{N_B-y}$, assuming $P(w|I)=p_I\to \frac{x}{N_I}andP(w|B)=p_B\to \frac{y}{N_B}$
    • saliency of a sentence
      • $weight(s)=\frac{1}{|S|}{\sum }_{w\in S}weight(w)$
      • only consider non-stop words in $S$

  • method 3: sentence centrality(find sentence )

    • Alternative approach to ranking sentences
    • Measure distance between sentences, and choose sentences that are closer to other sentences
    • Use tf-idf BOW to represent sentence
    • Use cosine similarity to measure distance
    • $centrality(s)=\frac{1}{\#sent}{\sum }_{s^{\prime }}co{s}_{tfidf}(s,s^{\prime })$
    • final extracted summary
      • Use top-ranked sentences as extracted summary
        • Saliency (tf-idf or log likelihood ratio)
        • Centrality

  • method 4: RST parsing

    • Rhetorical structure theory (L12, Discourse): explain how clauses are connected

    • Define the types of relations between a nucleus (main clause) and a satellite (supporting clause)

    • Nucleus more important than satellite

    • A sentence that functions as a nucleus to more sentences = more salient (dashed arrow is satellite, solid arrow is nucleus)

    • which sentence is the best summary sentence?

      • Mars experiences frigid conditions

Extractive: Multi-Doc

• summarisation system

  • Similar to single-document extractive summarisation system

  • Challenges:

    • Redundancy in terms of information(multiple documents contains the same information)
    • Sentence ordering(can on longer use the original order)

  • content selection

    • We can use the same unsupervised content selection methods (tf-idf, log likelihood ratio, centrality) to select salient sentences from each of these documents individually
    • But ignore sentences that are redundant

  • Maximum Marginal Relevance

    • Iteratively select the best sentence to add to summary
    • Sentences to be added must be novel(new information)
    • Penalise a candidate sentence if it’s similar to extracted sentences:
      • $MMR-penalty(s)=\lambda ma{x}_{s_i\in S}sim(s,s_i)$
      • $s_i$ is a extracted sentence
      • $s$ is a candidate sentence
      • $S$ is the set of extracted sentences
    • Stop when a desired number of sentences are added

  • Information Ordering

    • Chronological ordering:
      • Order by document dates
    • Coherence:
      • Order in a way that makes adjacent sentences similar
      • Order based on how entities are organised (centering theory, L12)

  • Sentence Realisation

    • Make sure entities are referred coherently
      • Full name at first mention
      • Last name at subsequent mentions
    • Apply coreference methods to first extract names
    • Write rules to clean up

Abstractive: Single-Doc

• example

  • Paraphrase
  • A very difficult task
  • Can we train a neural network to generate summary?

• Encoder-Decoder?

  • What if we treat:
    • Source sentence = “document”
    • Target sentence = “summary”

• data

  • News headlines

  • Document: First sentence of article

  • Summary: News headline/title

  • Technically more like a “headline generation task”

  • and it kind of works…

  • More Summarisation Data

    • But headline generation isn’t really exciting…
    • Other summarisation data:
      • CNN/Dailymail: 300K articles, summary in bullets
      • Newsroom: 1.3M articles, summary by authors
        • Diverse; 38 major publications
      • XSum: 200K BBC articles
        • Summary is more abstractive than other datasets

• improvements

  • Attention mechanism
  • Richer word features: POS tags, NER tags, tf-idf
  • Hierarchical encoders
    • One LSTM for words
    • Another LSTM for sentences

• Potential issues of an attention encoder-decoder summarisation system?

  • Has the potential to generate new details not in the source document (yes)
  • Unable to handle unseen words in the source document (yes, can only generate a closed set of words)
  • Information bottleneck: a vector is used to represent the source document (no, use attention)
  • Can only generate one summary (no)

• Copy Mechanism
  • Generate summaries that reproduce details in the document
  • Can produce out-of-vocab words in the summary by copying them in the document
    • e.g. smergle = out of vocabulary
    • p(smergle) = attention probability + generation probability = attention probability
• latest development
  • State-of-the-art models use transformers instead of RNNs
  • Lots of pre-training
  • Note: BERT not directly applicable because we need a unidirectional decoder (BERT is only an encoder)

Evaluation

• ROUGE (Recall Oriented Understudy for Gisting Evaluation)
  • Similar to BLEU, evaluates the degree of word overlap between generated summary and reference/human summary
  • But recall oriented(BLEU is precision-oriented)
  • Measures overlap in N-grams separately (e.g. from 1 to 3)
  • ROUGE-2: calculates the percentage of bigrams from the reference that are in the generated summary
• ROUGE-2: example

Conclusion

• Research focus on single-document abstractive summarisation
• Mostly news data
• But many types of data for summarisation:
  • Images, videos
  • Graphs
  • Structured data: e.g. patient records, tables
  • Multi-document abstractive summarisation